A nonlocal total variation model for image decomposition: Illumination and reflectance

In this paper, we study to use nonlocal bounded variation (NLBV) techniques to decompose an image intensity into the illumination and reflectance components. By considering spatial smoothness of the illumination component and nonlocal total variation (NLTV) of the reflectance component in the decomposition framework, an energy functional is constructed. We establish the theoretical results of the space of NLBV functions such as lower semicontinuity, approximation and compactness. These essential properties of NLBV functions are important tools to show the existence of solution of the proposed energy functional. Experimental results on both grey-level and color images are shown to illustrate the usefulness of the nonlocal total variation image decomposition model, and demonstrate the performance of the proposed method is better than the other testing methods.

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

[2]  S. Osher,et al.  A TV Bregman iterative model of Retinex theory , 2012 .

[3]  Aichi Chien,et al.  An L1-based variational model for Retinex theory and its application to medical images , 2011, CVPR 2011.

[4]  Jean-Michel Morel,et al.  Retinex Poisson Equation: a Model for Color Perception , 2011, Image Process. Line.

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

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

[7]  Xavier Bresson,et al.  Bregmanized Nonlocal Regularization for Deconvolution and Sparse Reconstruction , 2010, SIAM J. Imaging Sci..

[8]  Stanley Osher,et al.  Image Recovery via Nonlocal Operators , 2010, J. Sci. Comput..

[9]  Alessandro Rizzi,et al.  Pixel and spatial mechanisms of color constancy , 2010, Electronic Imaging.

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

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

[12]  Marcelo Bertalmío,et al.  Implementing the Retinex algorithm with Wilson–Cowan equations , 2009, Journal of Physiology-Paris.

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

[14]  Jean-Michel Morel,et al.  Fast implementation of color constancy algorithms , 2009, Electronic Imaging.

[15]  Michael Elad,et al.  Generalizing the Nonlocal-Means to Super-Resolution Reconstruction , 2009, IEEE Transactions on Image Processing.

[16]  Laurent D. Cohen,et al.  Non-local Regularization of Inverse Problems , 2008, ECCV.

[17]  Guy Gilboa,et al.  Nonlocal Operators with Applications to Image Processing , 2008, Multiscale Model. Simul..

[18]  Guy Gilboa,et al.  Nonlocal Linear Image Regularization and Supervised Segmentation , 2007, Multiscale Model. Simul..

[19]  Michael Elad,et al.  Retinex by Two Bilateral Filters , 2005, Scale-Space.

[20]  Stanley Osher,et al.  Deblurring and Denoising of Images by Nonlocal Functionals , 2005, Multiscale Model. Simul..

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

[22]  Stephen M. Smith,et al.  SUSAN—A New Approach to Low Level Image Processing , 1997, International Journal of Computer Vision.

[23]  John J. McCann,et al.  Retinex in MATLABTM , 2004, J. Electronic Imaging.

[24]  L. Ambrosio,et al.  Functions of Bounded Variation and Free Discontinuity Problems , 2000 .

[25]  Alan C. Evans,et al.  A nonparametric method for automatic correction of intensity nonuniformity in MRI data , 1998, IEEE Transactions on Medical Imaging.

[26]  Roberto Manduchi,et al.  Bilateral filtering for gray and color images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

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

[28]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

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

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

[31]  Leonid P. Yaroslavsky,et al.  Digital Picture Processing: An Introduction , 1985 .

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

[33]  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.

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

[35]  Berthold K. P. Horn,et al.  Determining lightness from an image , 1974, Comput. Graph. Image Process..

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