Abstract
Two image enhancement contrast methods are proposed in this paper for low-intensity images. The first method (LEAM) is a new greyscale mapping function, and it can be significantly enhanced in the low grey range and compressed slowly in the high grey range, which is beneficial for retaining more image details; the second method (LEAAM) is based on the data characteristics of a histogram combined with the first mapping function, which adaptively sets the gamma value to correct the image. The experimental results show that compared with a traditional mapping function, LEAM is more effective at enriching image details and enhancing visual effects, and LEAAM, compared with a recent low-illumination image enhancement algorithm, achieves good performance for average gradient, information entropy and contrast index; additionally, the overall visual effect is the best compared with other methods.
Similar content being viewed by others
References
Abdullah-Al-Wadud M, Kabir MH, Dewan M, Chae O A Dynamic Histogram Equalization for Image Contrast Enhancement, IEEE Trans Consum Electron on, vol. 53, pp. 593–600, 2007.
Al-Ameen Z (2019) Nighttime image enhancement using a new illumination boost algorithm. IET Image Process 13(8):1314–1320
Arici T, Dikbas S, Altunbasak Y (2009) A Histogram Modification Framework and Its Application for Image Contrast Enhancement. IEEE Trans Image Process 18(9):1921–1935
Cai B, Xu X, Guo K, Jia K, Hu B, Tao D (2017) A Joint Intrinsic-Extrinsic Prior Model for Retinex, in. IEEE International Conference on Computer Vision (ICCV) 2017:4020–4029
Cai J, Gu S, Zhang L (2018) Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images[J]. IEEE Transactions on Image Processing 27(4):2049–2062
Chang Y, Jung C, Ke P, Song H, Hwang J (2018) Automatic Contrast Limited Adaptive Histogram Equalization with Dual Gamma Correction[J]. IEEE Access 1–1
Dai Q, Pu YF, Rahman Z, Aamir M (2019) Fractional-Order Fusion Model for Low-Light Image Enhancement. Symmetry 11(4):574
Fu X, Zeng D, Huang Y, Liao Y, Ding X, Paisley J (2016) A fusion-based enhancing method for weakly illuminated images. Signal Process 129:82–96
Guo X (2016) LIME: A Method for Low-light IMage Enhancement
Guo X, Li Y, Ling H (2017) LIME: Low-Light Image Enhancement via Illumination Map Estimation. IEEE Trans Image Process 26(2):982–993
Huang S, Cheng F, Chiu Y (2013) Efficient Contrast Enhancement Using Adaptive Gamma Correction With Weighting Distribution. IEEE Trans Image Process 22(3):1032–1041
Huang Z, Zhang T, Li Q, Fang H (2016) Adaptive gamma correction based on cumulative histogram for enhancing near-infrared images. Infrared Phys Technol 79:205–215
Jenifer S, Parasuraman S, Kadirvelu A (2016) Contrast enhancement and brightness preserving of digital mammograms using fuzzy clipped contrast-limited adaptive histogram equalization algorithm. Appl Soft Comput 42:167–177
Salas JGG, Lisani JL (2011) Local Color Correction. IPOL J 1
Jin W, Huang H, Qiu Y, Wu H, Jian L (2005) Remote sensing image fusion based on average gradient of wavelet transform. In: Mechatronics and Automation, 2005 IEEE International Conference
Khan MA, Akram T, Sharif M et al (2019) An implementation of optimized framework for action classification using multilayers neural network on selected fused features. Pattern Anal Applic 22:1377–1397
Khan MA, Akram T, Sharif M, Javed K, Raza M, Saba T (2020) An automated system for cucumber leaf diseased spot detection and classification using improved saliency method and deep features selection. Multimed Tools Appl 79(25):18627–18,656
Kim M, Chung MG (2008) Recursively separated and weighted histogram equalization for brightness preservation and contrast enhancement. IEEE Trans Consum Electron 54(3):1389–1397
Li C, Guo J, Porikli F, Pang Y (2018) Lighten Net: A Convolutional Neural Network for weakly illuminated image enhancement. Pattern Recog Lett 104:15–22
Liao X, Li K, Yin J, “Separable data hiding in encrypted image based on compressive sensing and discrete fourier transform,” Multimed Tools Applic, vol. 76, no. 20, pp. 20739–20,753, 2017
Liao X, Qin Z, Ding L (2017) Data embedding in digital images using critical functions. Signal Process Image Commun: S0923596517301364
Amna L, Attique KM, Hussain SJ et al (2018) Automated ulcer and bleeding classification from wce images using multiple features fusion and selection. J Mech Med 18(04):1850038
Liaqat A et al (2020) Gastric Tract Infections Detection and Classification from Wireless Capsule Endoscopy using Computer Vision Techniques: A Review. Curr Med Imaging. https://doi.org/10.2174/1573405616666200425220513
Liu C, Sui X, Liu Y, Kuang X, Gu G, Chen Q (2019) Adaptive contrast enhancement based on histogram modification framework. J Modern Optics 66(15):1590–1601
Lore KG, Akintayo A, Sarkar S (2017) LLNet: A deep autoencoder approach to natural low-light image enhancement. Pattern Recogn 61:650–662
Moroney N (2000) Local color correction using nonlinear masking. pp 108–111
Nasir M, Khan MA, Sharif M, Lali IU, Saba T, Iqbal T (2018) An improved strategy for skin lesion detection and classification using uniform segmentation and feature selection based approach. Microsc Res Tech. https://doi.org/10.1002/jemt.23009
Ren Y, Ying Z, Li TH, Li G (2019) LECARM: Low-Light Image Enhancement Using the Camera Response Model. IEEE Trans Circ Syst Video Technol 29(4):968–981
Shannon CE (2001) A mathematical theory of communication. ACM SIGMOBILE Mobile. Comput Commun Rev 5(1):3–55
Sharif et al (2018) Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection. Comput Electron Agric 150:220–234
Sheet D, Garud H, Suveer A, Mahadevappa M, Chatterjee J (2010) Brightness preserving dynamic fuzzy histogram equalization. IEEE Trans Consum Electron 56(4):2475–2480
Singh K, Kapoor R (2014) Image enhancement using Exposure based Sub Image Histogram Equalization. Pattern Recog Lett 36:10–14
Singh K, Kapoor R (2014) Image enhancement via Median-Mean Based Sub-Image-Clipped Histogram Equalization. Optik 125(17):4646–4651
Singh K, Kapoor R, Sinha S (2015) Enhancement of low Exposure Images via Recursive Histogram Equalization Algorithms. Optik - Int J Light Electron Optics 126(20):2619–2625
Soong-Der C, Ramli AR (2003) Minimum mean brightness error bi-histogram equalization in contrast enhancement. IEEE Trans Consum Electron 49(4):1310–1319
Wang W, Chen Z, Yuan X, Wu X (2019) Adaptive image enhancement method for correcting low-illumination images. Inform Ences 496:25–41. https://doi.org/10.1016/j.ins.2019.05.015
Yeong-Taeg K (1997) Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Trans Consum Electron 43(1):1–8
Ying Z, Li G, Ren Y, Wang R, Wang W (2017) A new low-light image enhancement algorithm using camera response model. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW) Venice, pp 3015–3022
Ying Z, Li G, Ren Y, Wang R, Wang W (2017) A new image contrast enhancement algorithm using exposure fusion framework. In: International Conference on Computer Analysis of Images and Patterns In: Felsberg M, Heyden A, Krüger N (eds) Computer Analysis of Images and Patterns. CAIP 2017. Lecture Notes in Computer Science. Springer, Cham, pp 36–46
Ying Z, Li G, Ren Y, Wang R, Wang W (2017) A new low-light image enhancement algorithm using camera response model. IEEE International Conference on Computer Vision Workshops (ICCVW), Venice, pp 3015–3022
Yu W, Qian C, Baeomin Z (1999) Image enhancement based on equal area dualistic sub-image histogram equalization method. IEEE Trans Consum Electron 45(1):68–75
Zahid I, Attique KM, Muhammad S, Hussain SJ, u. R. M. Habib, and J. Kashif, (2018) An automated detection and classification of citrus plant diseases using image processing techniques: A review. Comput Electron Agric 153:12–32
Zahoor S, Lali IU, Khan MA, Javed K, Mehmood W (2020) Breast cancer detection and classification using traditional computer vision techniques: A Comprehensive Review. Curr Med Imaging Rev. https://doi.org/10.2174/1573405616666200406110547
Zuiderveld K (1994) Contrast Limited Adaptive Histogram Equalization. Graphics Gems 474–485
Funding
The financial supports by the Ministry of human resources and social security (198606), Science and Technology Department of Sichuan Province (2020JDRC0026), as well as the special fund for central finance of universities (2018SCU12065).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled “Two low illuminance image enhancement algorithms based on grey level mapping”.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Cheng, H., Long, W., Li, Y. et al. Two low illuminance image enhancement algorithms based on grey level mapping. Multimed Tools Appl 80, 7205–7228 (2021). https://doi.org/10.1007/s11042-020-09919-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-020-09919-x