A Probabilistic Method for Image Enhancement With Simultaneous Illumination and Reflectance Estimation

In this paper, a new probabilistic method for image enhancement is presented based on a simultaneous estimation of illumination and reflectance in the linear domain. We show that the linear domain model can better represent prior information for better estimation of reflectance and illumination than the logarithmic domain. A maximum a posteriori (MAP) formulation is employed with priors of both illumination and reflectance. To estimate illumination and reflectance effectively, an alternating direction method of multipliers is adopted to solve the MAP problem. The experimental results show the satisfactory performance of the proposed method to obtain reflectance and illumination with visually pleasing enhanced results and a promising convergence rate. Compared with other testing methods, the proposed method yields comparable or better results on both subjective and objective assessments.

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

[2]  E H Land,et al.  Recent advances in retinex theory and some implications for cortical computations: color vision and the natural image. , 1983, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Hwang Soo Lee,et al.  Adaptive local tone mapping based on retinex for high dynamic range images , 2013, 2013 IEEE International Conference on Consumer Electronics (ICCE).

[4]  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.

[5]  Bo Gu,et al.  Local Edge-Preserving Multiscale Decomposition for High Dynamic Range Image Tone Mapping , 2013, IEEE Transactions on Image Processing.

[6]  A Hurlbert,et al.  Formal connections between lightness algorithms. , 1986, Journal of the Optical Society of America. A, Optics and image science.

[7]  Hai-Miao Hu,et al.  Naturalness Preserved Enhancement Algorithm for Non-Uniform Illumination Images , 2013, IEEE Transactions on Image Processing.

[8]  Demetri Terzopoulos,et al.  Image Analysis Using Multigrid Relaxation Methods , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Hongyi Liu,et al.  Variational Bayesian Method for Retinex , 2014, IEEE Transactions on Image Processing.

[10]  E. Land,et al.  Lightness and retinex theory. , 1971, Journal of the Optical Society of America.

[11]  Liangpei Zhang,et al.  A spatially adaptive retinex variational model for the uneven intensity correction of remote sensing images , 2014, Signal Process..

[12]  Byung Cheol Song,et al.  Local tone mapping using sub-band decomposed multi-scale retinex for high dynamic range images , 2014, 2014 IEEE International Conference on Consumer Electronics (ICCE).

[13]  Robert A. Hummel,et al.  Image Enhancement by Histogram transformation , 1975 .

[14]  Zia-ur Rahman,et al.  Retinex processing for automatic image enhancement , 2002, IS&T/SPIE Electronic Imaging.

[15]  Mark S. Drew,et al.  Recovering Shading from Color Images , 1992, ECCV.

[16]  Albert A. Michelson,et al.  Studies in Optics , 1995 .

[17]  Wotao Yin,et al.  An Iterative Regularization Method for Total Variation-Based Image Restoration , 2005, Multiscale Model. Simul..

[18]  Edoardo Provenzi,et al.  A Perceptually Inspired Variational Framework for Color Enhancement , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Stanley Osher,et al.  Non-Local Retinex - A Unifying Framework and Beyond , 2015, SIAM J. Imaging Sci..

[20]  John J. McCann,et al.  Lessons Learned from Mondrians Applied to Real Images and Color Gamuts , 1999, CIC.

[21]  Alan C. Bovik,et al.  Making a “Completely Blind” Image Quality Analyzer , 2013, IEEE Signal Processing Letters.

[22]  Ralph R. Martin,et al.  Using retinex for point selection in 3D shape registration , 2014, Pattern Recognit..

[23]  K. Hohn,et al.  Determining Lightness from an Image , 2004 .

[24]  M. Ng,et al.  A nonlocal total variation model for image decomposition: Illumination and reflectance , 2014 .

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

[26]  William F. Schreiber,et al.  Fundamentals of Electronic Imaging Systems , 1986 .

[27]  Andrew Blake,et al.  Boundary conditions for lightness computation in Mondrian World , 1985, Comput. Vis. Graph. Image Process..

[28]  Tom Goldstein,et al.  The Split Bregman Method for L1-Regularized Problems , 2009, SIAM J. Imaging Sci..

[29]  Michael K. Ng,et al.  A Total Variation Model for Retinex , 2011, SIAM J. Imaging Sci..

[30]  E. Land.  Recent advances in retinex theory , 1986, Vision Research.

[31]  Byung Cheol Song,et al.  Power-Constrained Contrast Enhancement Algorithm Using Multiscale Retinex for OLED Display , 2014, IEEE Transactions on Image Processing.

[32]  Shengdong Pan,et al.  Adapting iterative retinex computation for high-dynamic-range tone mapping , 2013, J. Electronic Imaging.

[33]  Brian A. Wandell,et al.  A spatial extension of CIELAB for digital color‐image reproduction , 1997 .

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

[35]  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..

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

[37]  Alessandro Rizzi,et al.  Perceptual Color Correction Through Variational Techniques , 2007, IEEE Transactions on Image Processing.

[38]  Stanley Osher,et al.  A unifying retinex model based on non-local differential operators , 2013, Electronic Imaging.

[39]  Pascal Getreuer,et al.  Automatic Color Enhancement (ACE) and its Fast Implementation , 2012, Image Process. Line.

[40]  Chunming Li,et al.  Image segmentation with simultaneous illumination and reflectance estimation: An energy minimization approach , 2009, 2009 IEEE 12th International Conference on Computer Vision.

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

[42]  Edoardo Provenzi,et al.  Issues About Retinex Theory and Contrast Enhancement , 2009, International Journal of Computer Vision.

[43]  Licheng Jiao,et al.  Eye detection under varying illumination using the retinex theory , 2013, Neurocomputing.

[44]  Jean-Michel Morel,et al.  A PDE Formalization of Retinex Theory , 2010, IEEE Transactions on Image Processing.