Retinex-based image enhancement framework by using region covariance filter
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Clear images are critical in understanding real scenarios. However, the quality of images may be severely declined due to terrible conditions. Images exposed to such conditions are usually of low contrast, contain much noise, and suffer from weak details. And these drawbacks tend to negatively influence the subsequent processing tasks. Many existing image enhancement methods only solve a certain aspect of aforementioned drawbacks. This paper proposes a Retinex-based image enhancement framework that can increase contrast, eliminate noise, and enhance details at the same time. First, we utilize a region covariance filter to estimate the illumination accurately at multiple scales. The corresponding reflectance can be predicted by dividing the original image by its illumination. Second, we utilize contrast-limited adaptive histogram equalization to enhance the global contrast of original images because the illumination contains the low-frequency component. Third, since the reflectance contains the details of the original image and noise, we adopt a non-local means filter to eliminate noise and use a guided filter to enhance the details in the reflectance. Fourth, we synthesize the final enhanced image by fusing the enhanced illumination and reflectance at each scale. Experiments have proved the improvement of the proposed framework in terms of both visual perception and quantitative comparisons with other compared methods.
KeywordsRetinex Image enhancement Multiple scales Illumination estimation
The study was funded by the National Natural Science Foundation of China (No. 61711540303), Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) Fund, and Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET) Fund.
Compliance with ethical standards
Conflict of interest
All authors declare that they have no conflict of interest.
This article does not contain any studies with animals performed by any of the authors.
- Ahn H, Keum B, Kim D, Lee HS (2013) Adaptive local tone mapping based on retinex for high dynamic range images. In: IEEE international conference on consumer electronics, pp 153–156Google Scholar
- Asha M, Gupta MK (2016) A basic approach to enhance a gray scale image. Imp J Interdiscip Res 2(7):126–129Google Scholar
- Buades A, Coll B, Morel JM (2005) A non-local algorithm for image denoising. In: IEEE computer society conference on computer vision and pattern recognition, pp 60–65Google Scholar
- Cheng H, Zhang Y (2012) Detecting of contrast over-enhancement. In: 2012 19th IEEE international conference on image processing, pp 961–964. IEEEGoogle Scholar
- Choudhury A, Medioni G (2009) Perceptually motivated automatic color contrast enhancement. In: ICCV 2009—CRICV workshop, vol 7525, no 1, pp 1893–1900Google Scholar
- Elad M (2005) Retinex by two bilateral filters. Springer, BerlinGoogle Scholar
- Fallah M, Azizi A (2010) Quality assessment of image fusion techniques for multisensor high resolution satellite images (case study: IRS-p5 and IRS-P6 satellite images). Years Isprs Adv Remote Sensingence Pt 38(1):204–209Google Scholar
- Gu K, Zhai G, Yang X, Zhang W (2014) Automatic contrast enhancement technology with saliency preservation. IEEE Trans Circuits Syst Video Technol 25(9):1–1Google Scholar
- Maini R, Aggarwal H (2010) A comprehensive review of image enhancement techniques. Comput Sci 2(3):8–13Google Scholar
- Narsimha B, Suresh E, Chandar KP, Komuraiah B (2010) Enhancement of color images in hot domain with quantitative measurements using entropy and relative entropy In: International conference on signal and image processing, pp 34–38Google Scholar
- Pan Z, Lei J, Zhang Y, Sun X, Kwong S (2016) Fast motion estimation based on content property for low-complexity h. 265/hevc encoder. IEEE Trans Broadcast. 62(3):675–684. doi: 10.1109/TBC.2016.2580920
- Paris S, Hasinoff SW, Kautz J (2011) Local Laplacian filters: edge-aware image processing with a Laplacian pyramid. ACM Trans Graph 30(4, article 68):1244–1259Google Scholar
- Rahman ZU, Jobson DJ, Woodell GA (1996) Multi-scale retinex for color image enhancement. In: Proceedings of the international conference on image processing, 1996, vol 3, pp 1003–1006Google Scholar
- Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images. In: International conference on computer vision, pp 839–846Google Scholar
- Tuzel O, Porikli F, Meer P (2006) Region covariance: a fast descriptor for detection and classification. In: Proceedings of the computer vision—ECCV 2006, European conference on computer vision, Graz, Austria, May 7–13, 2006, pp 589–600Google Scholar
- Xia Z, Wang X, Zhang L, Qin Z, Sun X, Ren K (2016) A privacy-preserving and copy-deterrence content-based image retrieval scheme in cloud computing. IEEE Trans Inf Forensics Secur 11(11):2594–2608Google Scholar
- Xu L, Lu C, Xu Y, Jia J (2011) Image smoothing via L0 gradient minimization. ACM Trans Graph 30(6):61–64Google Scholar
- Xu L, Yan Q, Xia Y, Jia J (2012) Structure extraction from texture via relative total variation. ACM Trans Graph 31(6):439–445Google Scholar
- Yuan T, Zheng X, Hu X, Zhou W, Wang W (2013) A method for the evaluation of image quality according to the recognition effectiveness of objects in the optical remote sensing image using machine learning algorithm. Linear Algebra Appl 244(3):69–80Google Scholar
- Zhang Q, Shen X, Xu L, Jia J (2014) Rolling guidance filter. In: Fleet D, Pajdla T, Schiele B, Tuytelaars T (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8691. Springer, ChamGoogle Scholar
- Zheng Y, Byeungwoo J, Xu D, Wu QMJ, Zhang H (2015) Image segmentation by generalized hierarchical fuzzy c-means algorithm. J Intell Fuzzy Syst 28(2):4024–4028Google Scholar
- Zuiderveld K (1994) Contrast limited adaptive histogram equalization. In: Graphics gems IV. Academic Press Professional, pp 474–485Google Scholar