Advertisement

A new evaluation function for face image enhancement in unconstrained environments using metaheuristic algorithms

  • Muhtahir OloyedeEmail author
  • Gerhard Hancke
  • Hermanus Myburgh
  • Adeiza Onumanyi
Open Access
Research

Abstract

Image enhancement is an integral component of face recognition systems and other image processing tasks such as in medical and satellite imaging. Among a number of existing image enhancement methods, metaheuristic-based approaches have gained popularity owing to their highly effective performance rates. However, the need for improved evaluation functions is a major research concern in the study of metaheuristic-based image enhancement methods. Thus, in this paper, we present a new evaluation function for improving the performance of metaheuristic-based image enhancement methods. Essentially, we applied our new evaluation function in conjunction with metaheuristic-based optimization algorithms in order to select automatically the best enhanced face image based on a linear combination of different key quantitative measures. Furthermore, different from other existing evaluation functions, our evaluation function is finitely bounded to determine easily whether an image is either too dark or too bright. This makes it better suited to find optimal solutions (best enhanced images) during the search process. Our method was compared with existing metaheuristic-based methods and other state-of-the-art image enhancement techniques. Based on the qualitative and quantitative measures obtained, our approach is shown to enhance facial images in unconstrained environments significantly.

Keywords

Pre-processing Image enhancement Metaheuristic algorithm Unconstrained environments 

Abbreviations

AHE

Adaptive histogram equalization

AMBE

Absolute mean brightness error

BPDFHE

Brightness preserving dynamic fuzzy histogram equalization

CSO

Cuckoo search optimization

EF

Evaluation function

GA

Genetic algorithm

HE

Histogram equalization

IET

Image enhancement technique

IIA

Image intensity adjustment

LCS

Linear contrast stretching

LLIE

Low-light image enhancement

PSNR

Peak signal-to-noise ratio

PSO

Particle swarm optimization

1 Introduction

Pre-processing in face recognition systems involves enhancing an input face image in order to improve its quality by making more facial features in the image visible. Pre-processing enhances the performance of face recognition techniques [1, 2]. Further, the pre-processing stage amends distorted images and acquires regions of interest in an image for onward feature extraction. One important pre-processing task is image enhancement, which is essential to improve the performance of face recognition systems. However, most face recognition systems do not often incorporate face image enhancers in their designs, and whenever they do incorporate them, these are often less effective methods prior to the recognition process [3, 4].There are many image enhancement methods used in face recognition systems today. These methods have their pros and cons. Some recent methods transform an input image in order to achieve a more detailed or less noisy output image [5, 6]. In this regard, an image enhancement technique (IET) is considered effective if it is self-adaptive, i.e., if it adjusts its parameters to improve its performance automatically over different images. An IET must enhance an input image without introducing overstretching, excessive brightness, or loss of important features [7]. They should be simple with low computational complexity [8, 9]. These qualities are desired in a typical IET.

However, in face recognition systems, the quality of face images in unconstrained environments may be notably degraded for many reasons such as lighting conditions, i.e., in dark or too bright environments. Also, various facial expressions, change of pose, occluded faces, and other facial conditions may change the appearance of face images by hiding important features in the face [10]. Some IETs have been developed for various image-processing tasks; however, researchers have done minimal work concerning the development of IETs for face recognition in unconstrained environments. Further, most IETs often produce less enhanced outputs or unnatural effects and over enhancement in some cases that negatively affect the performance of face recognition systems. For these reasons, experts need to develop better IETs to improve further the performance of face recognition systems.

In terms of existing IETs, the histogram equalization (HE) method is a popular and simple approach for image enhancement [11, 12]. It works by adjusting the image’s contrast either by increasing or decreasing the global contrast of the image, especially when the image is characterized by close contrast values [13]. It operates by spreading the intensities of image pixels based on the information from the entire image. This results in conditions where low occurring intensities are transformed and fused with neighboring high occurring intensities, thus leading to over enhancement [14]. Further, mean shift issues may arise in such a situation, which maintains the image’s brightness and limits the performance of HE. The bi-histogram equalization (BHE) method was developed to address this problem [15], and it displayed better performance while maintaining the quality of the original image. However, the BHE is limited when the image pixel distribution is not symmetrical. Other methods such as the gamma correction and logarithm transformations have been used with lower computational complexities. However, they cannot manage complex illumination differences. Furthermore, other extensions of HE have been proposed such as the block-based histogram equalization, oriented local histogram equalization, and adaptive histogram equalization (AHE) methods. Nevertheless, these methods typically underperform in face recognition tasks, particularly in complex illumination conditions. This poor performance occurs because these methods rescind the entire distribution, which may contain important image characteristics [10].

Singh and Kapoor [16] presented an exposure-based sub-image histogram equalization method for contrast enhancement in low exposure grayscale images. Their method obtains thresholds, which are computed in order to split the original image into sub-images of various intensity levels. To control the enhancement rate, the histogram is clipped utilizing a threshold value as an average number of gray-level occurrences. Their method performed better than other conventional histogram equalization approaches. However, their approach does not adjust the level of enhancement, thereby resulting in darker or brighter enhanced images. Zhuang and Guan [17] used the mean and variance-based sub-image histogram equalization method to increase the contrast of the input image with brightness while retaining important features. However, because some IETs produce over-enhanced images and artifacts, Hussain et al. [8] proposed a dark image enhancement approach where local transformation of the image pixels is considered. The experiments in [8] showed that their method improved satisfactorily the quality of images. However, artifacts were present in the images. Reddy et al. [12] presented the dynamic clipped histogram equalization for enhancing low contrast images. Their approach selects a clipped level at the occupied bins and conducts histogram equalization on the clipped histogram to produce the output image. Reddy’s method uses three variants of the occupied bin space to enhance the low-contrasted dark, bright, and gray images. Shi et al [2] presented a dual channel prior-based method for nighttime low illumination image enhancement using a single image that is based on two existing image priors i.e., bright and dark priors. They used the bright channel prior to obtain the initial transmission estimate and used the dark prior as a complementary channel to adjust any wrong transmission estimate produced by the bright channel prior.

Linear contrast stretching (LCS) is an IET that uses linear transformation to increase the dynamic range of gray levels present in an original image [18]. LCS improves the contrast grade of an image; however, the LCS’s threshold value must be manually configured. If a wrong threshold value is used, the quality of the enhanced image will be low. Since no universal standard exists for image quality assessment, it becomes difficult to improve on an image by simply stretching its histogram or utilizing simple gray-level transformations [19, 20].

Thus, to solve these general issues, particularly when computers need to decide autonomously how good an enhanced image is, researchers have recently proposed methods based on evolutionary computation and metaheuristic optimization algorithms [21]. Metaheuristic algorithms are generally used to finding solutions involving non-linear optimization tasks. Munteanu and Rosa [5] pioneered the application of metaheuristic algorithms for image enhancement. They used an evaluation function (EF) to select automatically the most appropriate enhanced image without requiring human intervention. Thus, they proposed a novel EF and an evolutionary algorithm to globally search for the best enhanced image from among a solution space of candidate-enhanced images.

An EF plays a vital role in metaheuristic-based image enhancement methods. It automatically selects the best enhanced image by assessing the image’s quality without human involvement. Martens and Meesters [22] proposed an EF by using the statistical variable of the enhanced image. However, it could only be applied to a small set of test images. In 2015, Ye et al. [13] enhanced low contrast images by using a combination of the cuckoo search optimization (CSO) and the particle swarm optimization (PSO) algorithms. They derived contrast enhancement by the global transformation of the input intensities, while using an incomplete beta function as the transformation function. Three factors, namely the entropy value, threshold, and the probability density of the image, were used in [13] to measure the quality of the enhanced image. They evaluated and compared their approach to other IETs and showed improved performances. However, their model does not select always the most enhanced image, which necessitates the need for better methods.

Following in this paper, we present a metaheuristic-based IET for enhancing face images in an automatic and more effective manner than previous approaches. The primary contributions of our research are as follows:
  1. 1.

    We present a new EF as a component within the IET for face images.

     
  2. 2.

    We present a defined scaling mechanism for the EF in order to ensure that extreme values of an enhanced image depict either completely dark or completely white images.

     
  3. 3.

    Our proposed IET exhibits stability in selecting the most enhanced image based on both qualitative and quantitative measures.

     
  4. 4.

    Extensive quantitative and qualitative experiments were carried out to compare different existing standard EFs.

     
  5. 5.

    A comprehensive evaluation of the different standard metaheuristic-based algorithms was carried out.

     

The remainder of the paper is organized as follows: Section 2 discusses the proposed IET, where the related functions used are highlighted. Section 3 describes the data samples and the performance evaluation used. Section 4 discusses the experiments and simulation results compared with various state-of-the-art algorithms. Section 5 concludes the research work giving highlights of the merits of our proposed algorithm and scope for future research work.

2 Proposed method

2.1 Image enhancement technique

An IET uses an effective transformation function to efficiently map the intensity values of an original input image in order to produce an enhanced output image [23]. In metaheuristic-based methods, this process requires that an EF selects automatically the optimal enhancement parameters of a transformation function in order to appropriately enhance an image [24]. This section describes the transformation function used in our research, our proposed EF, and the metaheuristic algorithm that we considered.

2.2 Transformation function

Generally, the process of enhancing images in the spatial domain requires the use of a transformation function, which assigns new intensity values to each image pixel of the original image in order to produce an enhanced image. Local enhancement approaches apply transformation functions based on the gray-level distribution in the neighborhood of every pixel in a given input image. In our research, we used the transformation function proposed by Munteanu and Rosa in [5]. Munteanu’s approach applies a transformation function, T, to each pixel at a location (i, j) using the gray-level intensity of the pixel within the input image, f(i, j), and converts these intensities to another value, g(i, j), i.e., the gray-level intensity of the output image. The horizontal and vertical size of the image is denoted as Hsize and Vsize, respectively. Hence, T is defined according to [5] as:
$$ g\left(i,j\right)=T\left(f\left(i,j\right)\right)=k\left(\frac{M}{\sigma \left(i,j\right)+b}\right)\cdot \left[f\left(i,j\right)-c\cdot m\left(i,j\right)\right]+m{\left(i,j\right)}^a $$
(1)
where m(i, j) and σ(i, j) represent the mean and standard deviation of the gray scale image obtained for the pixels in the neighborhood centered at (i, j). The global mean, M, of the original image is computed as \( M={\sum}_{i=0}^{H_{\mathrm{size}-1}}{\sum}_{j=0}^{V_{\mathrm{size}-1}}f\left(i,j\right) \). The parameters a, b, c, and k in Eq. (1) have the following effects: Parameter a introduces a brightening bias in the output image based on the last term m(i, j) in Eq. (1). It enables further control over the amount of smoothening effect required in the output image. Parameter b ensures that a zero-standard deviation value in the local neighborhood pixels does not have a huge whitening effect on the final output image. Thus, by its introduction, the denominator component in Eq. (1) typically remains nonzero. Parameter c allows only a fraction of the mean, m(i,j), to be subtracted from the original pixels of the input image. The parameter c controls the degree of darkening introduced in the output image. Parameter k is introduced to create a fair balance between pixels existing in the mid-range boundaries of the gray scale. Essentially, these pixels are prevented from being either too dark or too white during the enhancement process. The following parameter values were noted to be highly effective based on an extensive empirical parameter-tuning exercise conducted in our work: 2 ≤ a ≤ 2.5, 0.3 ≤ b ≤ 0.5;  0 ≤ c ≤ 3, and 3 ≤ k ≤ 4. These values formed the limits of the constraints used in the optimization process of our method.

2.3 Evaluation function

An EF is used to assess the quality of an enhanced image, g(i, j) without the need for visual assessment by a human operator. It is used to determine the optimal parameter values of the transformation function, T, which produces the best enhanced image. The development of our EF was motivated following three ideas, which we describe as follows: Firstly, we began by identifying particular key metrics in the literature that suitably describe how well an image is enhanced or not. We considered as many metrics as possible, which differentiates our model from existing models in the literature. Secondly, we introduced the concept of normalizing the metrics in our model in order to provide a bound for our method. This innovation made it possible to define a linear function that differs from existing functions in the literature. Interestingly, by this innovation, our method defines specific boundaries for images in very black and very white regions. These clear boundaries further enable an optimization algorithm to find better solutions in a well-defined range. Thirdly, we investigated three different optimization methods in order to use the best method, which led to the choice of the cuckoo search optimization algorithm. We present evidence of its performance in Section 4.

Thus, in developing our EF, and similar to [25], we quantified the following qualities of a well-enhanced image as follows: A well enhanced image should have a higher number of edge pixels than the original image. Furthermore, an enhanced image is expected to have a higher measure of information than the original image. This measure of information can be quantified using an entropic metric such as the histogram of the image. This approach is similar to the information measure used in the histogram equalization technique. Similarly, more pixels belonging to the foreground objects in an enhanced image should be better revealed than in the original image. An enhanced image should contain less alien artifacts than the original image. Based on the above qualities of an enhanced image, we propose a new EF that comprises of different performance metrics. These metrics, which are used to measure the qualities mentioned above, include the number of edge pixels, number of foreground pixels, entropic measure, and the peak signal-to-noise ratio (PSNR).

We describe the process of our EF as follows: First, the number of edge pixels, Ng, in the enhanced image is computed. To achieve this, a Sobel threshold, Tf, is automatically computed from the original image, f(i, j), using the Sobel edge detector. This threshold, Tf, is then used in the Sobel edge detector to obtain the edge intensities, Eg(i, j), of the enhanced image. In addition to being invariant, Tf was considered in our EF for computing Eg(i, j) in order to ensure a fair comparison between the original image and the different instances of the enhanced image. Thus, the number of edge pixels, Ng, in the enhanced image is obtained as:
$$ {N}_g=\sum \limits_{i=1}^H\sum \limits_{j=1}^V{E}_g\left(i,j\right) $$
(2)

Secondly, the number of pixels, ϕg, belonging to the foreground objects in g(i, j) is computed. To achieve this, the variance ϑg(i, j), of g(i, j), and the variance ϑf(i, j), of f(i, j), are computed within a neighborhood (window) having n × n pixels. A threshold value, ηf, is automatically computed for ϑf(i, j) using Otsu’s threshold algorithm. A representation, Dg(i, j), revealing pixels belonging to the foreground objects in the enhanced image is obtained as:

$$ {D}_g\left(i,j\right)=\left\{\begin{array}{c}1\kern1em \mathrm{if}\ {\vartheta}_g\left(i,j\right)\ge {\eta}_f\\ {}\\ {}0\kern1em \mathrm{if}\ \mathrm{otherwise}\kern0.75em \end{array}\right.\kern0.5em \mathrm{for}\ i=1,2,\dots, H;j=1,2,\dots, V. $$
(3)
Thus, ϕg, is obtained as
$$ {\phi}_g=\sum \limits_{i=1}^H\sum \limits_{j=1}^V{D}_g\left(i,j\right) $$
(4)
Thirdly, an entropic measure, βg, of g(i, j) is computed as
$$ {\beta}_g=\left\{\begin{array}{c}-\sum \limits_m{\Omega}_m\log \left({\Omega}_m\right)\kern0.75em \mathrm{for}\ {\Omega}_m\ne 0\\ {}\\ {}0\kern5.25em \mathrm{for}\ {\Omega}_m\ne 0\end{array}\right. $$
(5)
where Ωm is the frequency of pixels having gray levels in the histogram bin, m = 1, … , 256. The PSNR, ρg, of g(i, j) is obtained as
$$ {\rho}_g=10{\log}_{10}\left[\frac{{\left(L-1\right)}^2}{\mathrm{MSE}}\right] $$
(6)
where L is the maximum pixel intensity value in g(i, j) and MSE is given as
$$ \mathrm{MSE}=\frac{1}{H\times V}\sum \limits_{i=1}^H\sum \limits_{j=1}^V{\left|f\left(i,j\right)-g\Big(i,j\Big)\right|}^2 $$
(7)
Based on the parameters computed in Eqs. (2)–(7), a new EF, Ε, is proposed as
$$ E=1-\exp \left(-\frac{\rho_g}{100}\right)+\frac{N_g+{\phi}_g}{H\times V}+\frac{\beta_g}{8} $$
(8)
where Ε is a linear combination of the normalized values of the different metrics described in Eqs. (2)–(7). By normalizing each metric in Eq. (8), we confine each parameter to values between 0 and 1. Thus, based on this linear combination, our EF is described by a defined scale bounded between a minimum value of 0 and maximum value of 4. A minimum value of 0 represents an entirely black enhanced image, while a maximum value of 4 represents an entirely white enhanced image.

2.4 Metaheuristic algorithm

Metaheuristic optimization algorithms generally find solutions for highly non-linear optimization tasks [26]. They follow an iterative process that directs a subordinate heuristic by automatically fusing various ideas to explore a solution search space. Learning strategies are utilized to arrange information in order to find efficient optimal solutions. There are different types of metaheuristic algorithms known to be effective, such as the particle swarm optimization (PSO), genetic algorithm (GA), and the cuckoo search optimization (CSO) algorithms. In our research work, the CSO was used following an extensive comparison of the different metaheuristic algorithms as presented in the result section (see Section 4.2). The CSO algorithm was developed in [27], and it is among the most recent metaheuristic algorithms used for global optimization, with a look-alike process of the brood parasitic behavior of certain cuckoo species. The CSO was considered for its simplicity, fast convergence, and for its useful capability proven over series of experiments carried out in [28]. These attributes are essential requirements, which we considered worthwhile in order to enhance the contrast of images. The CSO algorithm based on Levy flight was applied in our work for searching new solutions based on the model given as:
$$ {a_i}^{\left(c+1\right)}={a_i}^{(c)}+\alpha \otimes \mathrm{Levy}\left(\lambda \right) $$
(9)
where ai(c + 1) depicts the latest solutions for a cuckoo, i, using flight function, with λ representing the Levy walk parameter, α represents the step size associated to the scale of the problem of interest, and ⊗ product represents entry wise multiplication. The Levy flight produces a random walk while the arbitrary step length is derived from a Levy distribution as Levy ∼u = tλ, (1 < λ ≤ 3). Generally, the process of the CSO is described as follows: every egg present in a nest denotes a solution and a cuckoo egg depicts a new solution. The purpose is to utilize the latest and supposedly better solutions, i.e., cuckoo to swap the less efficient solutions in the nests [27]. The adoption of the CSO in our work considers the situation for just an egg, as we are interested in only one solution. Table 1 describes the algorithmic process in the CSO. In the next section, we describe the entire flow process involved in our proposed image enhancement method based on an integration of the transformation function, our proposed EF, and the CSO algorithm.
Table 1

Steps involved in the Proposed Face Image Enhancement Technique

Inputs: Input (original) image, lower and upper values of each parameter (constraints)—stated in Section 2.2

Outputs: Final enhanced image, optimal parameter values

1. The face image is acquired, resized, and converted to gray scale as f(i, j)

2. The values of the lower and upper constraints for each parameter in the transformation function (see Eq. 1) are defined.

3. The CSO algorithm is initiated as follows:

 3.1. Let the number of nests be n, and the dimension of each particle be D, which corresponds to the number of variables to be optimized in our algorithm. In this case, D = 4, representing the four different parameters to be optimized in Eq. (1). The probability of discovering an alien egg or solution in a nest is given as Pa, while the number of iterations of the CSO algorithm is given as S.

 3.2. The random and initial solutions (nests) for each parameter are generated

 3.3. For every CSO iteration, until S, do

 3.4. Use Levy flights to obtain a new solution for each nest

 3.5. Evaluate each solution (nest) using Eq. (8)

 3.6. The best value among all the nests is obtained as Εmax

 3.7. If \( {E}_{S+1}^{\mathrm{max}}>{E}_S^{\mathrm{max}} \)

 3.8. Update the new global best

 3.9. End if

 3.10. Empty a fraction of the worst nests based on Pa

 3.11. Update each new solution using Eq. (9)

 3.12. Keep the best nests

 3.13. Return to step 3.4 until S is completed

4. Obtain the optimized values of each parameter after the CSO iterations are completed

5. Use the optimized values in Eq. (1) to obtain a final enhanced image, g(i, j)

2.5 Summary of the proposed image enhancement algorithm

In this section, we provide a summary of our proposed image enhancement method as stipulated in Table 1. Typically, the inputs to our method are the original image to be enhanced and the range of values describing the lower and upper constraints of each parameter. The process converts the original image to its corresponding gray scale image. This gray scale image is passed to the CSO algorithm where the entire optimization process selects the best enhanced image. Essentially, we describe the process involved in a single iteration of the CSO algorithm. However, the iteration typically continues until a stopping criterion is met or there is no further change in the fitness value.

Firstly, the original gray scale image is subjected to the transformation function in Eq. (1) to obtain a supposed enhanced image. The transformation function is applied based on a set of initial parameter values selected by the CSO algorithm in a random manner. These parameter values are used in the transformation function in order to obtain a supposed enhanced image. This enhanced image is then passed to the EF were a specific value is computed for the enhanced image as described in Section 2.3. This EF value is one of several possible values computed for different enhanced images belonging to a population of possible enhanced images. Thus, each enhanced image in the population is subjected to the transformation function using different sets of parameter values and then passed to the EF to obtain a fitness value. The best (or largest) EF value from among a population of different enhanced images is selected per generation (or iteration). The iteration proceeds until no better value is obtained. Thus, the enhanced image that produces the highest EF value after several number of iterations (or generation) is outputted as the best enhanced image.

3 Performance evaluation and data samples used

In our work, we enhanced face images in unconstrained environments. Thus, we used face images obtained from three different standard benchmark face datasets, i.e., the AR face database [29], Yale face dataset (YF), and the Olivetti research laboratory face dataset (ORL). Face images affected by different lighting conditions, different facial expressions, and different pose variations were selected from each face database. The lighting conditions used were from the right, left, and both sides. Further, for the different facial expressions, face images with smile, anger, and scream were selected.

We describe the protocol used to select the representative images considered in the evaluation of our method and other state-of-the art approaches as follows: Essentially, six different images were selected based on our protocol. Firstly, since our research focused on facial images and their constraints, we used six different types of representative facial conditions in unconstrained environments, namely smile, anger, scream, right light illumination, left light illumination, and both side illumination. Secondly, we categorized all images in each dataset into these six different facial conditions. Thirdly, one representative image from each category was randomly selected, thus accounting for the six different images presented in Section 4 per dataset.

The effectiveness of an IET can be measured qualitatively by visualizing the enhanced output image. However, it is also required to describe quantitatively the degree of enhancement of an image. We describe the following metrics used to assess quantitatively the measure of enhancement of an image. The metrics considered are number of edges, number of pixels in the foreground, entropic measure, PSNR, and absolute mean brightness error (AMBE), which are defined in the following sub-sections.

3.1 Number of edges

The number of edges produced by an IET must provide a more substantial number of edges on the enhanced image as compared to the original input image. A higher number of edges on the enhanced image are desired as compared to the input image. The number of edges Ng can be obtained as stated in Eq. (2).

3.2 Number of pixels in the foreground

An effective IET must be able to reveal more pixels that belong to the foreground object in the enhanced image as compared to the original image. Hence, a higher value of the number of pixels in the foreground is desired to quantify the effectiveness of an IET. The number of pixels in the foreground ϕg can be obtained as stated in Eq. (4).

3.3 Entropic measure

An entropic measure is regarded as the process of quantifying the details of information in the image. The larger the entropic measure value, the more detailed an enhanced image will be. Also, the entropic value of an image is independent of a different image because comparison is done on the same image before and after the processing [30]. The entropic measure of the enhanced image βg can be obtained as stated in Eq. (5).

3.4 PSNR

An IET must not only have the ability to improve the images but also control the level at which artifacts is introduced into the enhanced image, i.e., the level of noise should not be increased during the enhancement process. The PSNR ρg is used to evaluate the increase in quality between the original and the enhanced image [31]. The PSNR value can be obtained as stated in Eq. (6).

3.5 AMBE

The AMBE, ξ, is generally used to measure the rate at which the mean brightness is preserved, which can be represented mathematically as in Eq. (10). It shows the change in mean brightness value between the original and the enhanced image. Furthermore, the mean brightness of the original and enhanced image can be calculated as shown in Eqs. (11) and (12), respectively. Thus, a lower AMBE value is desired, while a zero AMBE value is considered the ideal result.
$$ \xi =\mid \delta \left(f\left(i,j\right)\right)-\delta \left(g\left(i,j\right)\right)\mid, $$
(10)
$$ \delta \left(f\left(i,j\right)\right)=\frac{1}{HV}\sum \limits_i\sum \limits_j\delta \left(f\left(i,j\right)\right), $$
(11)
$$ \delta \left(g\left(i,j\right)\right)=\frac{1}{HV}\sum \limits_i\sum \limits_j\delta \left(g\left(i,j\right)\right) $$
(12)
where δ(f(i, j)) depicts the mean brightness of the original image and δ(g(i, j)) represents the mean brightness of the enhanced image.

4 Simulation results and discussion

In this section, we discuss the effects of various experiments carried out in our research work. Firstly, an experiment to determine the choice of appropriate CSO parameters was conducted. Then, simulations using different metaheuristic algorithms were conducted to assess the respective performances of each algorithm based on the fitness value and time of convergence. Furthermore, we carried out experiments to compare our function with other EFs using the CSO algorithm in order to verify the effectiveness of our proposed EF. Finally, to confirm the efficacy of our image enhancement method, quantitative and qualitative comparisons were conducted based on standard performance metrics across different standard benchmark datasets.

4.1 Choice of CSO parameter

To effectively select appropriate values of the parameter, Pa, for the CSO algorithm, an experiment with different values ranging between Pa = 0.1 to 1.0 was conducted. Results were plotted based on the number of iterations and the fitness value. It is seen in Fig. 1 below that Pa = 0.2 converged the earliest. Hence, in our research, we selected Pa = 0.2.
Fig. 1

Choice of CSO parameter

4.2 Evaluation of different metaheuristic algorithms

To confirm the selection of the metaheuristic algorithm used in our research, an evaluation of different metaheuristic algorithms was carried out. The algorithms considered include the CSO, PSO, and genetic algorithm (GA) methods. The number of iterations for each metaheuristic algorithm was set at 100, which we plotted against the fitness value for each metaheuristic algorithm. In order to avoid bias, we selected three different images from the AR dataset and evaluated the performance of the different algorithms as shown in Fig. 2.
Fig. 2

Performance and the convergence analysis of the different metaheuristic algorithms on different images. a Image 1. b Image 2. c Image 3

In Fig. 2, it is seen that across the different images labeled (a), (b), and (c), the CSO algorithm outperformed other metaheuristic algorithms. For image 1, the fitness function value obtained by the CSO is 1.534; followed closely by the PSO at a fitness value of 1.532, and lastly the GA with a fitness value of 1.4509. For image 2, the fitness function of the CSO reached a value of 1.31, which is followed by the PSO value of 1.304 and GA obtained a value of 1.302. Similarly, evaluating the different algorithms on image 3, the CSO algorithm achieved the highest fitness function value of 1.417, while being followed by the PSO algorithm with a fitness function value of 1.406, and lastly the GA with a fitness function value of 1.404. The convergence analysis of the different algorithms was also observed with the CSO algorithm converging the earliest across all images at the 20th, 63rd, and 95th iteration for images 1–3, respectively. Furthermore, as shown in Table 2, we compared the different metaheuristic algorithms based on our proposed EF using all the performance evaluation metrics such as the number of pixels in the foreground, number of edges, PSNR, entropic measure, AMBE, and fitness value.
Table 2

Comparison of the different metaheuristic algorithm with our proposed EF based on all the performance evaluation metrics

Metrics

CSO + Proposed EF

PSO + Proposed EF

GA + Proposed EF

Im1

Im2

Im3

Im1

Im2

Im3

Im1

Im2

Im3

ϕ g

6026

2014

4567

4267

2022

4318

5806

2019

4291

N g

3408

2422

2475

2475

2412

2437

3148

2410

2421

ρ g

11.9

13.2

12.4

12.4

12.5

11.4

10.2

12.5

11.5

β g

7.7

7.7

7.7

7.7

7.7

7.7

7.2

7.7

7.5

ξ

0.0

0.1

0.0

0.0

0.1

0.0

0.2

0.1

0.0

Ε

1.534

1.310

1.417

1.532

1.304

1.406

1.4509

1.302

1.404

Legend: ϕg number of pixels in the foreground; Ng number of edge pixels; ρg PSNR; βg entropic measure; ξ absolute mean brightness error; Ε fitness value

The various performance evaluation metrics are defined in Section 3. From Table 2, the CSO algorithm in conjunction with our EF provided exciting and useful results reported as follows for image 1; the number of pixels in the foreground value of 6026 was attained for the CSO outperforming PSO and GA with values of 4267 and 5806, respectively. Similarly, in image 3, the highest number of pixels in the foreground value of 4567 was attained using the CSO with our proposed algorithm, outperforming the values of 4318 and 4291 generated by PSO and GA, respectively. Furthermore, the values generated for the number of edges on the different images by the CSO algorithm with the proposed EF outperformed all other techniques. This implies that the CSO algorithm with the proposed EF is able to improve on the original images, thereby revealing more information in the image.

To confirm our selection of the CSO metaheuristic algorithm, we qualitatively analyzed the enhanced images produced by the different metaheuristic algorithms with the proposed EF as shown in Figs. 3, 4, and 5. We inserted red bounding boxes in Figs. 3, 4, 5, 6, 7, and 8 to emphasize particular regions of interest. For example, observe the bounding box in the original image of Fig. 3 and notice that the beards of the subject are significantly revealed in the subsequent enhanced images (see Fig. 3b, c, and d). We applied our method based on different metaheuristic algorithms and showed qualitatively (see Figs. 3, 4, and 5) that there is not much to differentiate between these optimization algorithms by qualitative analysis. However, we showed slight advantages of the CSO algorithm over other methods by the quantitative results presented in Table 2. Furthermore, consider Fig. 4 and observe that the four spots within the bounding box in the original image are unobvious; however, they are clearly enhanced and made visible in the enhanced images (see Fig. 4b, c, and d). Similarly, we show bounding boxes on other images to emphasize interesting features that have been clearly enhanced over the respective original images. In essence, the CSO algorithm provided better quantitative performance as compared to the PSO and GA algorithms.
Fig. 3

Qualitative comparison of the different metaheuristic algorithm with our proposed evaluation function on Image 1. a Original. b CSO + proposed evaluation method. c PSO + proposed evaluation method. d GA + proposed evaluation method

Fig. 4

Qualitative comparison of the different metaheuristic algorithm with our proposed evaluation function on Image 2. a Original. b CSO + proposed evaluation method. c PSO + proposed evaluation method. d GA + proposed evaluation method

Fig. 5

Qualitative comparison of the different metaheuristic algorithm with our proposed evaluation function on Image 3. a Original. b CSO + proposed evaluation method. c PSO + proposed evaluation method. d GA + proposed evaluation method

Fig. 6

Qualitative comparison of the different evaluation function with the CSO algorithm on Image 1. a Original. b Munteanu + CSO. c Ye + CSO. d Proposed + CSO

Fig. 7

Qualitative comparison of the different evaluation function with the CSO algorithm on Image 2. a Original. b Munteanu + CSO. c Ye + CSO. d Proposed + CSO

Fig. 8

Qualitative comparison of the different evaluation function with the CSO algorithm on Image 3. a Original. b Munteanu + CSO. c Ye + CSO. d Proposed + CSO

4.3 Comparison of different EFs

In this section, we confirm the performance of our proposed EF based on the CSO algorithm. Hence, we compared the different EFs used in Munteanu [5] and Ye [13] using the CSO algorithm based on three different selected face images that vary among individuals as shown in Table 3.
Table 3

Quantitative comparison for different EFs

Metrics

Munteanu + CSO (M_CSO)

Ye + CSO

Proposed + CSO

Im1

Im2

Im3

Im1

Im2

Im3

Im1

Im2

Im3

ϕ g

1867

1082

2301

762

927

1935

2104

1116

4827

N g

537

361

630

1314

1673

1719

2306

2057

3368

ρ g

14.9

13.3

17.0

17.7

15.7

17.8

14.5

13.8

12.9

β g

7.8

7.7

7.7

7.8

7.6

7.8

7.8

7.7

7.8

ξ

0.1

0.1

0.0

0.0

0.0

0.0

0.0

0.1

0.0

Ε

1.235

1.160

1.279

1.242

1.227

1.323

1.333

1.252

1.510

Considering Table 3, the different EFs were compared based on all the performance evaluation metrics using the CSO algorithm. The results obtained confirm the effectiveness of our proposed EF by displaying decent results reported as follows: across images 1, 2, and 3, our proposed EF produced the highest foreground value of 2104, 1116, and 4827, respectively as compared to the other EFs. Similarly, values generated for the number of edges by our proposed algorithms outperformed other EF techniques with higher values of 2304, 2057, and 3368 for images 1, 2, and 3, respectively. Furthermore, the fitness value generated by the different algorithms was analyzed, and it showed that our proposed algorithm produced the highest fitness value for all the images.

To confirm the effectiveness of our proposed EF, a comparison of the different EFs was conducted by analyzing qualitatively the enhanced images as shown in Figs. 6, 7, and 8. Following these figures, it is evident that our proposed EF outperforms the other algorithms.

4.4 Comparison of different image enhancement methods

The experiments in this section were designed to confirm the performance of our proposed algorithm by comparing our method with other state-of-the-art image enhancement methods. The methods considered are the linear contrast stretching (LCS), histogram equalization (HE), image intensity adjustment (IIA), Munteanu with particle swarm optimization (M_PSO), Munteanu with genetic algorithm (M_GA), brightness preserving dynamic fuzzy histogram equalization (BPDFHE), contrast limited adaptive histogram equalization (CLAHE), and low-light image enhancement (LLIE). The results obtained are presented in Tables 4, 5, 6, 7, 8, and 9. We conducted these experiments using different standard benchmark face datasets by selecting six different face images with different real-world conditions. The chosen face images selected from each face dataset were labeled images 1–6, and they represent multiple faces with lightning conditions, pose variations, and facial expressions such as smile, anger, scream, left light, right light, and both light on, respectively.
Table 4

Number of pixels in the foreground value comparison between results obtained using different enhancement methods on standard benchmark face datasets

Database

LCS

HE

IIA

M_PSO

M_GA

BPDFHE

CLAHE

LLIE

Proposed

AR

 Image 1

451

1006

451

2565

2249

391

1560

977

3294

 Image 2

399

1220

399

2407

2388

334

1248

655

2565

 Image 3

937

2174

937

3582

3669

891

2660

1734

5424

 Image 4

989

1572

989

3491

3475

740

2283

895

5956

 Image 5

636

677

636

2902

2912

462

2134

961

6865

 Image 6

383

887

383

2317

2324

190

651

371

3947

YF

 Image 1

1308

9563

1308

5693

5955

950

1249

1439

13,117

 Image 2

1515

13,388

1515

8914

9129

1359

1966

1175

17,694

 Image 3

2285

6298

2285

8547

7003

2098

2010

3006

12,425

 Image 4

1589

14,331

1589

7806

8105

1347

2004

1034

18,071

 Image 5

1712

4731

1712

5936

5324

1508

1356

834

9751

 Image 6

1887

5555

1887

6824

6080

1686

1515

1004

10,483

ORL

 Image 1

482

489

482

1795

1909

333

1066

623

3873

 Image 2

1938

2191

1938

5277

5279

1318

4209

2873

7618

 Image 3

1784

2167

1784

3926

3928

1246

3218

2385

6499

 Image 4

1438

1556

1438

3324

2877

1014

2658

1552

6248

 Image 5

1330

1016

1330

4312

4353

1025

2513

1958

5946

 Image 6

915

738

915

1792

1912

559

1240

1152

4705

Table 5

Number of edge value comparison between results obtained using different enhancement methods on standard benchmark face datasets

Database

LCS

HE

IIA

M_PSO

M_GA

BPDFHE

CLAHE

LLIE

Proposed

AR

 Image 1

624

1240

624

2395

2175

684

1981

1362

2889

 Image 2

522

1375

522

2089

2071

514

1882

1010

2223

 Image 3

696

1315

696

1976

2016

683

1866

1186

2756

 Image 4

781

1017

781

1993

1974

665

1627

992

3044

 Image 5

726

672

726

2072

2072

570

1915

1073

3776

 Image 6

700

1496

700

1898

1901

733

1657

671

3247

YF

 Image 1

1842

4580

1842

5028

5213

1604

2946

2546

9247

 Image 2

1699

6158

1699

5727

5804

1514

4064

2631

9106

 Image 3

1756

2829

1756

4825

4013

1677

2151

2328

6517

 Image 4

1521

7184

1521

5464

5509

2514

4234

2604

9013

 Image 5

1349

2103

1349

3780

3409

1217

1845

2209

5461

 Image 6

1409

2377

1409

3982

3514

1307

2153

2095

5490

ORL

 Image 1

458

546

458

1124

1195

354

911

569

1898

 Image 2

603

713

603

1633

1634

395

1293

890

2532

 Image 3

714

812

714

1325

1326

517

1122

877

2203

 Image 4

668

751

668

1334

1197

519

1346

753

2313

 Image 5

566

465

566

1436

1450

470

955

725

2021

 Image 6

602

524

602

944

987

424

945

775

1865

Table 6

Peak signal-to-noise ratio value comparison between results obtained using different enhancement methods on three different standard benchmark face datasets

Database

LCS

HE

IIA

M_PSO

M_GA

BPDFHE

CLAHE

LLIE

Prop

AR

 Image 1

23.135

17.921

23.135

14.430

15.239

27.1569

17.021

13.99

13.259

 Image 2

21.184

15.375

21.848

12.396

12.392

34.8048

16.348

14.281

12.6282

 Image 3

25.490

19.090

25.490

15.746

15.318

32.3678

18.724

13.833

12.437

 Image 4

24.003

14.126

24.003

12.846

12.428

40.5588

16.8669

17.726

8.8844

 Image 5

24.360

15.6500

24.360

14.5273

14.4144

40.5269

17.5406

17.654

9.147

 Image 6

24.2599

9.8793

24.259

14.1864

14.17

29.9462

17.1313

21.347

8.244

YF

 Image 1

27.071

12.472

27.071

14.101

13.269

34.115

23.451

26.014

8.069

 Image 2

36.541

12.581

36.541

13.711

13.757

35.481

20.531

15.104

8.197

 Image 3

37.271

17.085

37.271

13.207

6.607

37.806

21.755

23.831

9.759

 Image 4

42.581

13.731

42.581

14.659

14.048

25.339

19.812

14.259

8.837

 Image 5

39.031

18.110

39.030

2.2967

3.328

32.593

20.954

14.969

9.499

 Image 6

35.343

16.072

35.343

13.392

5.065

37.973

21.706

17.007

9.027

ORL

 Image 1

25.323

20.849

25.323

17.551

16.441

31.981

18.153

16.097

11.635

 Image 2

26.787

22.484

26.787

14.861

14.831

41.335

16.407

14.606

10.258

 Image 3

25.591

22.103

25.591

16.318

16.212

37.621

17.695

18.665

10.188

 Image 4

23.788

19.598

23.788

15.441

16.947

33.865

17.825

18.590

10.523

 Image 5

25.624

28.241

25.624

15.371

15.381

36.755

17.314

16.071

12.158

 Image 6

22.703

21.727

22.703

18.523

18.139

32.633

17.743

15.356

11.829

Table 7

Entropic measure value comparison between results obtained using different enhancement methods on standard benchmark face datasets

Database

LCS

HE

IIA

M_PSO

M_GA

BDPFHE

CLAHE

LLIE

Proposed

AR

 Image 1

7.4463

5.9567

7.4463

7.7381

7.7415

7.2321

7.8153

7.4387

7.7312

 Image 2

7.1931

5.9504

7.1931

7.6573

7.6578

6.8574

7.6891

7.1804

7.8393

 Image 3

7.413

5.931

7.413

7.6654

7.661

7.0664

7.7904

7.2489

7.7804

 Image 4

7.635

5.9717

7.635

7.7815

7.786

7.2305

7.7595

7.0171

7.8593

 Image 5

7.5609

5.8266

7.5609

7.5745

7.5721

7.1865

7.708

7.0508

7.8258

 Image 6

7.0688

5.8843

7.0688

7.4651

7.4632

6.6483

7.5923

6.3949

7.3507

YF

 Image 1

4.2938

3.1034

4.2938

4.6969

4.6444

4.1962

4.4254

4.1136

4.5492

 Image 2

4.7103

3.4205

4.7103

4.8841

4.8882

4.5318

4.8105

4.1881

4.6918

 Image 3

4.6965

3.4164

4.6965

4.2898

4.0718

4.5828

4.8450

3.7340

4.9105

 Image 4

5.2128

3.8475

5.2128

5.2600

5.2511

4.9808

5.3399

4.7239

5.0825

 Image 5

3.8014

2.7607

3.8014

3.4162

3.4312

3.6916

3.9933

3.8220

3.9198

 Image 6

3.8665

2.7464

3.8665

3.6413

3.3834

3.7931

3.9946

3.7089

3.8903

ORL

 Image 1

7.2911

5.9766

7.2911

7.7474

7.7194

7.1604

7.7833

7.3308

7.4561

 Image 2

7.4287

5.9811

7.4287

7.7244

7.7241

7.3292

7.8739

7.4295

7.2415

 Image 3

7.3419

5.9719

7.3419

7.7132

7.705

7.2402

7.8589

7.5324

7.2677

 Image 4

7.3675

5.9542

7.3675

7.5777

7.6382

7.2482

7.8178

7.646

7.7055

 Image 5

7.3794

5.9636

7.3794

7.4041

7.3967

7.2621

7.8876

7.6182

7.9296

 Image 6

7.4323

5.9823

7.4323

7.6305

7.6103

7.2897

7.8693

7.7043

7.1986

Table 8

AMBE value comparison between results obtained using different enhancement methods on three different standard benchmark face datasets

Database

LCS

HE

IIA

M_PSO

M_GA

BDPFHE

CLAHE

LLIE

Proposed

AR

 Image 1

0.0657

0.0545

0.0657

0.0584

0.0488

0.0227

0.0654

0.1834

0.0598

 Image 2

0.0779

0.0606

0.0779

0.1332

0.1367

0.0124

0.0978

0.1824

0.1116

 Image 3

0.0491

0.0365

0.0491

0.0315

0.0173

0.0144

0.0326

0.1856

0.0369

 Image 4

0.0533

0.1721

0.0533

0.1647

0.1865

0.0045

0.0545

0.1124

0.1385

 Image 5

0.0483

0.1533

0.0483

0.1173

0.1209

0.0007

0.04

0.1028

0.1306

 Image 6

0.0506

0.2688

0.0506

0.1136

0.1135

0.0028

0.0862

0.0744

0.1136

YF

 Image 1

0.0255

0.1463

0.0255

0.1028

0.1219

0.0082

0.0028

0.0024

0.1153

 Image 2

0.0085

0.1339

0.0085

0.1351

0.1284

0.0059

0.0152

0.0993

0.1121

 Image 3

0.0087

0.0603

0.0087

0.0812

0.3344

0.0008

0.0342

0.0018

0.0833

 Image 4

0.0046

0.1121

0.0046

0.1128

0.1318

0.0231

0.0235

0.1156

0.1113

 Image 5

0.0065

0.0416

0.0065

0.6291

0.5493

0.0048

0.0436

0.1011

0.0547

 Image 6

0.0096

0.0712

0.0096

0.0717

0.4463

0.0279

0.0322

0.0745

0.0632

ORL

 Image 1

0.0164

0.0549

0.0164

0.0646

0.0797

0.0030

0.0141

0.1378

0.0656

 Image 2

0.0216

0.0495

0.0216

0.1332

0.1340

0.0029

0.0011

0.1680

0.1182

 Image 3

0.0040

0.0078

0.0040

0.0931

0.0957

0.0064

0.0366

0.0832

0.0834

 Image 4

0.0126

0.0397

0.0126

0.0861

0.0656

0.0012

0.0362

0.0945

0.0276

 Image 5

0.0059

0.0117

0.0059

0.0813

0.0763

0.0013

0.0363

0.1311

0.0792

 Image 6

0.0192

0.0614

0.0192

0.0459

0.0487

0.0162

0.0509

0.1478

0.0440

Table 9

Fitness value comparison between results obtained using different enhancement methods on standard benchmark face datasets

Database

LCS

HE

IIA

M_PSO

M_GA

BDPFHE

CLAHE

LLIE

Proposed

AR

 Image 1

1.1916

1.0221

1.1916

1.3521

1.3325

1.1961

1.3123

1.1785

1.4028

 Image 2

1.1419

1.0174

1.1419

1.3008

1.299

1.1939

1.267

1.1148

1.3154

 Image 3

1.2341

1.0914

1.2341

1.3846

1.3868

1.2357

1.3731

1.1828

1.4817

 Image 4

1.2572

1.009

1.2572

1.3702

1.3653

1.3082

1.326

1.1349

1.497

 Image 5

1.2301

0.9413

1.2301

1.3332

1.3325

1.2836

1.3289

1.1459

1.5531

 Image 6

1.1537

0.9486

1.1537

1.2783

1.2784

1.1364

1.223

1.0442

1.3613

YF

 Image 1

0.8143

0.6871

0.8143

0.8565

0.8484

0.8464

0.8161

0.7945

0.9337

 Image 2

0.9362

0.7971

0.9362

0.9269

0.9316

0.9021

0.8644

0.7126

1.0098

 Image 3

0.9501

0.7014

0.9501

0.8319

0.7145

0.9362

0.8546

0.7474

0.9629

 Image 4

1.0383

0.8859

1.0383

0.9645

0.9625

0.8961

0.9274

0.7701

1.0682

 Image 5

0.8376

0.5986

0.8376

0.5746

0.5739

0.7746

0.7293

0.6559

0.7012

 Image 6

0.8234

0.5937

0.8234

0.7194

0.5956

0.8285

0.7416

0.6598

0.7355

ORL

 Image 1

1.2263

1.0357

1.2263

1.4126

1.4177

1.2354

1.3308

1.1807

1.6019

 Image 2

1.4101

1.2308

1.4101

1.7742

1.7743

1.4209

1.6695

1.4297

1.9877

 Image 3

1.3859

1.2339

1.3859

1.6243

1.6227

1.3896

1.5657

1.4284

1.8498

 Image 4

1.3368

1.1461

1.3368

1.5423

1.5061

1.3421

1.5291

1.3491

1.8439

 Image 5

1.3324

1.3522

1.3324

1.6258

1.6303

1.3604

1.4815

1.3611

1.7788

 Image 6

1.2793

1.0655

1.2793

1.3884

1.3985

1.2851

1.3583

1.2924

1.6491

From Table 4, the performance of the different image enhancement methods was analyzed based on the number of pixels in the foreground, which represents the amount of information introduced to the enhanced image. Hence, the goal is to obtain a higher number of pixels in the foreground. Our proposed algorithm produced the highest number of pixels in the foreground value across all images within the different face datasets. For image 1 from the AR face dataset, the number of pixels in the foreground value was 3294 achieved by our method, followed by the M_PSO with a value of 2565, then M_GA with a value of 2249, and LCS with a value of 451. Our enhancement method produced for images 2–6 the following number of pixels in the foreground values: 2565, 5424, 5956, 6865, and 3947, respectively, which surpasses the values presented by other enhancement methods. For the YF face dataset, our method produced a significant number of pixel values in the foreground across all face images. Similarly, for the ORL face dataset, our method produced the largest number of pixels in the foreground based on the different selected face images. Using this dataset, the values produced for images 1–6 include 10,483, 3873, 7618, 6499, 6248, 5946, and 4705, respectively.

The number of edges produced by the different enhancement methods was compared across the different standard face datasets as shown in Table 5. Our method demonstrated a better performance based on the performance metric used across all images in the different datasets. We desire a higher value of the number of edges because it demonstrates that more information has been added to the enhanced image. M_GA follows with the largest number of edge values across all images, while LCS and BPDFHE produced the least number of edge values.

Tables 6, 7, and 8 display the PSNR value, entropic measure value, and the AMBE value, respectively. The PSNR value obtained with our method produced the lowest value across all face images within the different face datasets. This implies that a lower PSNR value produces a more enhanced image. The entropic value by our proposed technique produced a higher value for most face images as compared to the other methods. This shows that more information has been added to the enhanced image to make the face image unique. Further, the AMBE values of each method, representing the mean brightness value, were compared. Our method preserved the absolute mean brightness error value significantly. Furthermore, the fitness values of each method were computed and presented in Table 9. The fitness value is an important metric that determines the effectiveness of each technique, and a higher fitness value is desired. Our method when compared to the other IETs produced the highest fitness value across most face images used from the different face datasets. Our method is followed by the M_PSO method across all images. The CLAHE and BPDFHE methods produced a satisfactory performance while the HE method produced the least fitness value across all images.

To confirm further the performance of our proposed algorithm, we compared qualitatively the enhanced images produced by the different image enhancement methods as shown in Figs. 9, 10, and 11 with each figure representing different lightning conditions, pose variation, and facial expressions such as smile, anger, scream, left light on, right light on, and both light on, respectively.
Fig. 9

Qualitative comparison of the different image enhancement algorithms on the AR face dataset where Figs. 1, 2, 3, 4, 5, and 6 represent images of different subjects respectively. aj denote the methods labeled as a original, b LCC, c HE, d IIA, e M_PSO, f M_GA, g BDPFHE, h CLAHE, i LLIE, and j proposed

Fig. 10

Qualitative comparison of the different image enhancement algorithms on the Yale face dataset where Figs. 1, 2, 3, 4, 5, and 6 represent images of different subjects respectively. and aj denote the methods labeled as a original, b LCC, c HE, d IIA, e M_PSO, f M_GA, g BDPFHE, h CLAHE, i LLIE, and j proposed

Fig. 11

Qualitative comparison of the different image enhancement algorithms on the ORL face dataset where Figs. 1, 2, 3, 4, 5, and 6 represent images of different subjects respectively. aj denote the methods labeled as a original, b LCC, c HE, d IIA, e M_PSO, f M_GA, g BDPFHE, h CLAHE, i LLIE, and j proposed

Figures 9, 10, and 11 display the effectiveness of our proposed IETs. The enhanced images produced by our algorithm show a much positive difference in quality than the original and other enhanced images by other methods. The images affected by lighting conditions, i.e., images 4, 5, and 6 representing right light, left light and both light on, respectively were effectively enhanced as compared to other enhancement techniques. Furthermore, our method produced decent enhanced images considering images with different pose and expressions. Generally, unlike the images produced by other methods, our method produced more enhanced face images. This better performance was achieved because our method considers different essential metrics in its design. Metrics such as the number of edges and the number of the pixel value in the foreground will undoubtedly add more features to the image, thus, producing a more enhanced image.

4.5 Processing time per image

In this section, we provide results concerning the processing time of each algorithm per image considered in our research. We conducted the experiments in this section using a PC built on an Intel Core i5 processor with an Intel HD Graphics 4400 and 8GB RAM. We tested each algorithm using three different pixel sizes in order to assess their respective average processing times. The results obtained are presented in Table 10. Our algorithm experienced the longest average processing time per image. The processing time increased as the image pixel size increased. Our method experienced a longer processing delay because it computes iteratively both in the transformation and in the evaluation blocks for each potentially enhanced image in a population of several solutions. This iteration in the optimization process further prolonged the processing time of our method. This indicates that a trade-off thus exists between achieving better-enhanced images at the expense of speed. Though our method experienced a longer delay than other alternatives; however, this delay was at a far better enhancement performance than the other alternatives. Practical application areas where our method can be applied are as follows: in face enhancement and editing software in smartphones, face enhancement for forensic purposes, and face enhancement for cosmetic and dermatological purposes. In these and other similar application areas, experts are notably interested more in better-enhanced images than in the speed of enhancement. Nevertheless, we have observed that metaheuristic-based methods tend to provide improved performance than other methods at the expense of longer processing times.
Table 10

Processing time per facial image comparison using the different image enhancement methods on different image pixel size

Image pixel

Methods

LCS

HE

IIA

M_PSO

M_GA

BDPFHE

CLAHE

LLIE

Proposed

64 × 64

0.115

0.117

0.114

3.635

3.783

0.114

0.133

0.202

11.155

128 × 128

0.144

0.145

0.143

13.736

14.0451

0.140

0.151

0.231

27.363

256 × 256

0.250

0.254

0.250

54.373

54.778

0.248

0.248

0.321

90.126

5 Conclusion

Image enhancement is an essential pre-processing stage in typical face recognition systems. Hence, an efficient IET is required in order to improve face recognition performance. In this paper, we have presented a new EF for face image enhancement in unconstrained environments using metaheuristic algorithms. Our EF is used in conjunction with the cuckoo search optimization (CSO) algorithm to determine the best enhanced image, similar to the visual role played by a human evaluator. We achieved improved performance by introducing a scaled mechanism in our EF that prevents the enhanced image from assuming extreme dark or bright images. We have shown that our proposed EF outperforms other standard EFs. In addition, extensive quantitative and qualitative comparisons with other metaheuristic and state-of-the-art image enhancement methods were conducted in order to demonstrate the effectiveness of our method. We evaluated our method using different face images from different standard benchmark face datasets that represent different real-life scenarios. The performance metrics considered in our research demonstrated the superior performance of our method over other methods. For future works, we will look at the possibility of exploring other metaheuristic algorithms. In addition, approaches to reduce the processing time per image using the proposed method will be investigated. We note that our work can be extended to other application areas of image processing in order to improve their respective performance rates.

Notes

Acknowledgements

The authors will like to thank the editor, associate editor and peer reviewers for all their comments.

Funding

This work was supported by the Council for Scientific and Industrial Research (CSIR), South Africa. [ICT: Meraka].

Availability of data and materials

The AR, Yale and ORL face datasets were used to confirm our proposed methods.

Authors’ contributions

MO was the main contributor to the design of the new image enhancement algorithm and drafted the manuscript. GH and HM edited and modified the overall content of the manuscript while also giving adequate supervision. AO participated in the discussion of this work. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  1. 1.
    M.O. Oloyede, G.P. Hancke, Unimodal and multimodal biometric sensing systems: a review. IEEE Access 4, 7532–7555 (2016)CrossRefGoogle Scholar
  2. 2.
    Z. Shi, M. mei Zhu, B. Guo, M. Zhao, C. Zhang, Nighttime low illumination image enhancement with single image using bright/dark channel prior. EURASIP J. Image Video Proc. 2018(1), 13 (2018)CrossRefGoogle Scholar
  3. 3.
    N. Dagnes, E. Vezzetti, F. Marcolin, S. Tornincasa, Occlusion detection and restoration techniques for 3D face recognition: a literature review. Mach Vis Appl 29, 789–813 (2018)Google Scholar
  4. 4.
    M. Sharif, M.A. Khan, T. Akram, M.Y. Javed, T. Saba, A. Rehman, A framework of human detection and action recognition based on uniform segmentation and combination of Euclidean distance and joint entropy-based features selection. EURASIP J. Image Video Proc. 2017(1), 89 (2017)CrossRefGoogle Scholar
  5. 5.
    C. Munteanu, A. Rosa, Gray-scale image enhancement as an automatic process driven by evolution. IEEE Trans. Syst. Man Cybern. Part B (Cybernetics) 34(2), 1292–1298 (2004)CrossRefGoogle Scholar
  6. 6.
    H.-T. Wu, S. Tang, J.-L. Dugelay, Image reversible visual transformation based on MSB replacement and histogram bin mapping. in Proceedings of the IEEE Tenth International Conference on Advanced Computational Intelligence (ICACI) Xiamen, 813 –818 (2018)Google Scholar
  7. 7.
    C. Li, J. Guo, F. Porikli, Y. Pang, LightenNet: a convolutional neural network for weakly illuminated image enhancement. Pattern Recogn. Lett. 104, 15–22 (2018)CrossRefGoogle Scholar
  8. 8.
    K. Hussain et al., A histogram specification technique for dark image enhancement using a local transformation method. IPSJ Trans. Comput. Vision Appl. 10(1), 3 (2018)CrossRefGoogle Scholar
  9. 9.
    B.-V. Le, S. Lee, T. Le-Tien, Y. Yoon, Using weighted dynamic range for histogram equalization to improve the image contrast. EURASIP J. Image Video Proc. 2014(1), 44 (2014)CrossRefGoogle Scholar
  10. 10.
    Y. Cheng, L. Jiao, X. Cao, Z. Li, Illumination-insensitive features for face recognition. Vis. Comput. 33(11), 1483–1493 (2017)CrossRefGoogle Scholar
  11. 11.
    J.R. Tang, N.A.M. Isa, Bi-histogram equalization using modified histogram bins. Appl. Soft Comput. 55, 31–43 (2017)CrossRefGoogle Scholar
  12. 12.
    M. Barni, E. Nowroozi, B. Tondi, in Proceeding of the IEEE International Workshop on Biometrics and Forensics (IWBF), Sassari. Detection of adaptive histogram equalization robust against JPEG compression (2018), pp. 1–8Google Scholar
  13. 13.
    Z. Ye, M. Wang, Z. Hu, W. Liu, An adaptive image enhancement technique by combining cuckoo search and particle swarm optimization algorithm. Comput Intell Neurosci 2015, 13 (2015)Google Scholar
  14. 14.
    P.B. Aquino-Morínigo, F.R. Lugo-Solís, D.P. Pinto-Roa, H.L. Ayala, J.L.V. Noguera, Bi-histogram equalization using two plateau limits. SIViP 11(5), 857–864 (2017)CrossRefGoogle Scholar
  15. 15.
    X. Wang, L. Chen, Contrast enhancement using feature-preserving bi-histogram equalization. Signal Image and Video Processing, 12(4), 1–8 (2017)Google Scholar
  16. 16.
    K. Singh, R. Kapoor, Image enhancement using exposure based sub image histogram equalization. Pattern Recogn. Lett. 36, 10–14 (2014)CrossRefGoogle Scholar
  17. 17.
    L. Zhuang, Y. Guan, Image enhancement via subimage histogram equalization based on mean and variance. Comput Intell Neurosci 2017, 1–12 (2017)Google Scholar
  18. 18.
    A. Mustapha, A. Oulefki, M. Bengherabi, E. Boutellaa, M.A. Algaet, Towards nonuniform illumination face enhancement via adaptive contrast stretching. Multimed. Tools Appl. 76(21), 21961–21999 (2017)CrossRefGoogle Scholar
  19. 19.
    K. Hasikin, N.A.M. Isa, Adaptive fuzzy contrast factor enhancement technique for low contrast and nonuniform illumination images. SIViP 8(8), 1591–1603 (2014)CrossRefGoogle Scholar
  20. 20.
    S. Rahman, M.M. Rahman, M. Abdullah-Al-Wadud, G.D. Al-Quaderi, M. Shoyaib, An adaptive gamma correction for image enhancement. EURASIP J. Image Video Proc. 2016(1), 35 (2016)CrossRefGoogle Scholar
  21. 21.
    K.G. Dhal, S. Das, Cuckoo search with search strategies and proper objective function for brightness preserving image enhancement. Pattern Recog. Image Anal. 27(4), 695–712 (2017)CrossRefGoogle Scholar
  22. 22.
    J.-B. Martens, L. Meesters, Image dissimilarity. Signal Process. 70(3), 155–176 (1998)CrossRefGoogle Scholar
  23. 23.
    A. Bhandari, A. Kumar, S. Chaudhary, G. Singh, A new beta differential evolution algorithm for edge preserved colored satellite image enhancement. Multidim. Syst. Sign. Process. 28(2), 495–527 (2017)CrossRefGoogle Scholar
  24. 24.
    M. Abdel-Basset, A.-N. Hessin, L. Abdel-Fatah, A comprehensive study of cuckoo-inspired algorithms. Neural Comput. Applic. 29(2), 345–361 (2018)CrossRefGoogle Scholar
  25. 25.
    W. Xi, T. Wu, K. Yan, X. Yang, X. Jiang, N. Kwok, Restoration of online video ferrography images for out-of-focus degradations. EURASIP J. Image Video Proc. 2018(1), 31 (2018)CrossRefGoogle Scholar
  26. 26.
    J.-P. Pelteret, B. Walter, P. Steinmann, Application of metaheuristic algorithms to the identification of nonlinear magneto-viscoelastic constitutive parameters. J. Magn. Magn. Mater. 464, 116 (2018)CrossRefGoogle Scholar
  27. 27.
    X.-S. Yang and S. Deb, “Cuckoo search via Lévy flights,” in Proceeding of the IEEE World Congress on Nature & Biologically Inspired Computing, NaBIC Coimbatore 2009, pp. 210–214Google Scholar
  28. 28.
    B. Yang, J. Miao, Z. Fan, J. Long, X. Liu, Modified cuckoo search algorithm for the optimal placement of actuators problem. Appl. Soft Comput. 67, 48–60 (2018)CrossRefGoogle Scholar
  29. 29.
    A.M. Martinez, The AR face database, CVC Technical Report 24 (1998)Google Scholar
  30. 30.
    Z. Krbcova, J. Kukal, Relationship between entropy and SNR changes in image enhancement. EURASIP J. Image Video Proc. 2017(1), 83 (2017)CrossRefGoogle Scholar
  31. 31.
    S. Suresh, S. Lal, Modified differential evolution algorithm for contrast and brightness enhancement of satellite images. Appl. Soft Comput. 61, 622–641 (2017)CrossRefGoogle Scholar

Copyright information

© The Author(s). 2019

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Department of Electrical Electronic and Computer EngineeringUniversity of PretoriaPretoriaSouth Africa

Personalised recommendations