Retinex-Based Perceptual Contrast Enhancement in Images Using Luminance Adaptation

In this paper, we propose retinex-based perceptual contrast enhancement in images using luminance adaptation. Strong illumination causes the loss of local details in an image. We adopt luminance adaptation and multi-scale retinex (MSR) to successfully remove the illumination effect in an image while enhancing details. First, we estimate the illumination component in an image by adaptive smoothing and get luminance just-noticeable difference (JND) from it using luminance adaptation. Then, we calculate an illumination weakening factor from luminance JND and conduct MSR based on it to enhance details. Finally, we perform contrast enhancement using adaptive gamma correction with weighted distribution. Experimental results demonstrate that the proposed method successfully enhances details in an image while preventing over and under enhancement as well as outperforms state of the arts in terms of various quantitative measurements.

[1]  Soumik Sarkar,et al.  LLNet: A deep autoencoder approach to natural low-light image enhancement , 2015, Pattern Recognit..

[2]  Karel J. Zuiderveld,et al.  Contrast Limited Adaptive Histogram Equalization , 1994, Graphics Gems.

[3]  Chun-Hsien Chou,et al.  A perceptually tuned subband image coder based on the measure of just-noticeable-distortion profile , 1994, Proceedings of 1994 IEEE International Symposium on Information Theory.

[4]  Zia-ur Rahman,et al.  Properties and performance of a center/surround retinex , 1997, IEEE Trans. Image Process..

[5]  Abd. Rahman Ramli,et al.  Minimum mean brightness error bi-histogram equalization in contrast enhancement , 2003, IEEE Trans. Consumer Electron..

[6]  Myung-Ryul Choi,et al.  A contrast enhancement method using dynamic range separate histogram equalization , 2008, IEEE Transactions on Consumer Electronics.

[7]  Sos S. Agaian,et al.  No reference color image contrast and quality measures , 2013, IEEE Transactions on Consumer Electronics.

[8]  Weisi Lin,et al.  Just-noticeable difference estimation with pixels in images , 2008, J. Vis. Commun. Image Represent..

[9]  Nikil Jayant,et al.  Signal Compression: Technology Targets and Research Directions , 1992, IEEE J. Sel. Areas Commun..

[10]  Yücel Altunbasak,et al.  A Histogram Modification Framework and Its Application for Image Contrast Enhancement , 2009, IEEE Transactions on Image Processing.

[11]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[12]  Abd. Rahman Ramli,et al.  Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation , 2003, IEEE Trans. Consumer Electron..

[13]  Dong-Guk Hwang,et al.  Frankle-McCann Retinex by Shuffling , 2012, ICHIT.

[14]  Chao Wang,et al.  Brightness preserving histogram equalization with maximum entropy: a variational perspective , 2005, IEEE Trans. Consumer Electron..

[15]  Zia-ur Rahman,et al.  Multi-scale retinex for color image enhancement , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[16]  Bo Li,et al.  Image enhancement based on Retinex and lightness decomposition , 2011, 2011 18th IEEE International Conference on Image Processing.

[17]  Alessandro Rizzi,et al.  Mathematical definition and analysis of the retinex algorithm. , 2005, Journal of the Optical Society of America. A, Optics, image science, and vision.

[18]  Joonki Paik,et al.  Dual Autoencoder Network for Retinex-Based Low-Light Image Enhancement , 2018, IEEE Access.

[19]  E. Land.  The retinex theory of color vision. , 1977, Scientific American.

[20]  Yeong-Taeg Kim,et al.  Contrast enhancement using brightness preserving bi-histogram equalization , 1997 .

[21]  Xiaoyan Sun,et al.  Structure-Revealing Low-Light Image Enhancement Via Robust Retinex Model , 2018, IEEE Transactions on Image Processing.

[22]  John D. Austin,et al.  Adaptive histogram equalization and its variations , 1987 .

[23]  John J. McCann,et al.  Retinex in Matlab , 2000, CIC.

[24]  Zia-ur Rahman,et al.  A multiscale retinex for bridging the gap between color images and the human observation of scenes , 1997, IEEE Trans. Image Process..

[25]  Shih-Chia Huang,et al.  Efficient Contrast Enhancement Using Adaptive Gamma Correction With Weighting Distribution , 2013, IEEE Transactions on Image Processing.

[26]  Laurence Meylan,et al.  High dynamic range image rendering with a retinex-based adaptive filter , 2006, IEEE Transactions on Image Processing.

[27]  R. J. Safranek,et al.  A perceptually tuned sub-band image coder with image dependent quantization and post-quantization data compression , 1989, International Conference on Acoustics, Speech, and Signal Processing,.

[28]  Haidi Ibrahim,et al.  Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement , 2007, IEEE Transactions on Consumer Electronics.

[29]  Nam Chul Kim,et al.  Color Image Enhancement Based on Single-Scale Retinex With a JND-Based Nonlinear Filter , 2007, 2007 IEEE International Symposium on Circuits and Systems.

[30]  J. Gibbon.  Scalar expectancy theory and Weber's law in animal timing. , 1977 .

[31]  Peter G. J. Barten,et al.  Contrast sensitivity of the human eye and its e ects on image quality , 1999 .

[32]  Cheolkon Jung,et al.  Retinex-based perceptual contrast enhancement in images using luminance adaptation , 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[33]  David Zhang,et al.  FSIM: A Feature Similarity Index for Image Quality Assessment , 2011, IEEE Transactions on Image Processing.

[34]  Michael Elad,et al.  A Variational Framework for Retinex , 2002, IS&T/SPIE Electronic Imaging.

[35]  Jong Beom Ra,et al.  Enhancement of Optical Remote Sensing Images by Subband-Decomposed Multiscale Retinex With Hybrid Intensity Transfer Function , 2011, IEEE Geoscience and Remote Sensing Letters.

[36]  Qian Chen,et al.  Image enhancement based on equal area dualistic sub-image histogram equalization method , 1999, IEEE Trans. Consumer Electron..

[37]  Farhan A. Baqai,et al.  Analysis and extensions of the Frankle-McCann Retinex algorithm , 2004, J. Electronic Imaging.

[38]  Xiao-Ping Zhang,et al.  A novel retinex based approach for image enhancement with illumination adjustment , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[39]  Yu Li,et al.  LIME: Low-Light Image Enhancement via Illumination Map Estimation , 2017, IEEE Transactions on Image Processing.