Adapting iterative retinex computation for high-dynamic-range tone mapping

Abstract. Retinex algorithms have been widely applied in many aspects of image processing. Based on the iterative Retinex algorithm, we propose an edge-preserving illumination estimation method. Inspired by the anisotropic diffusion, an edge-stopping function is introduced in the iterative computation. This modification enables the preservation of abrupt edges when computing the upper envelope of a given image. Based on the illumination-reflectance decomposition, a high-dynamic-range (HDR) radiance map can be easily tone-mapped to be a low-dynamic-range image by compressing the range of the estimated illumination. Artifacts are effectively suppressed using the proposed method. Meanwhile, we also propose a jumping-spiral iteration manner to improve the symmetry of the edge response. Experimental results show that the proposed tone mapping algorithm is very effective in reproducing HDR scenes, and has a better performance compared with some similar operators.

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

[2]  Jinfeng Yang,et al.  Shadow removal using Retinex theory , 2011, 2011 Third Chinese Conference on Intelligent Visual Surveillance.

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

[4]  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..

[5]  Kate Devlin,et al.  A review of tone reproduction techniques , 2002 .

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

[7]  Cewu Lu,et al.  Image smoothing via L0 gradient minimization , 2011, ACM Trans. Graph..

[8]  Alan Chalmers,et al.  Evaluation of tone mapping operators using a High Dynamic Range display , 2005, ACM Trans. Graph..

[9]  Robert Sobol,et al.  Improving the Retinex algorithm for rendering wide dynamic range photographs , 2002, IS&T/SPIE Electronic Imaging.

[10]  E H Land,et al.  An alternative technique for the computation of the designator in the retinex theory of color vision. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[11]  Doron Shaked,et al.  Robust Recursive Envelope Operators for Fast Retinex , 2004 .

[12]  Hiroshi Yamaguchi,et al.  Evaluating HDR rendering algorithms , 2007, TAP.

[13]  Hans-Peter Seidel,et al.  Dynamic range independent image quality assessment , 2008, ACM Trans. Graph..

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

[15]  Jack Tumblin,et al.  The Trilateral Filter for High Contrast Images and Meshes , 2003, Rendering Techniques.

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

[17]  Karol Myszkowski,et al.  Design of a tone mapping operator for high-dynamic range images based upon psychophysical evaluation and preference mapping , 2003, IS&T/SPIE Electronic Imaging.

[18]  Yandong Tang,et al.  Shadow Detection and Removal from Solo Natural Image Based on Retinex Theory , 2008, ICIRA.

[19]  Zeev Farbman,et al.  Edge-preserving decompositions for multi-scale tone and detail manipulation , 2008, ACM Trans. Graph..

[20]  Alexei A. Efros,et al.  Fast bilateral filtering for the display of high-dynamic-range images , 2002 .

[21]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Mark S. Drew,et al.  Removing Shadows From Images using Retinex , 2002, CIC.

[23]  Jaeseok Kim,et al.  Natural hdr image tone mapping based on retinex , 2011, IEEE Transactions on Consumer Electronics.

[24]  Alessandro Rizzi,et al.  Random Spray Retinex: A New Retinex Implementation to Investigate the Local Properties of the Model , 2007, IEEE Transactions on Image Processing.

[25]  Luís Paulo Santos,et al.  A local model of eye adaptation for high dynamic range images , 2004, AFRIGRAPH '04.

[26]  Frédo Durand,et al.  Edge-preserving multiscale image decomposition based on local extrema , 2009, ACM Trans. Graph..

[27]  Hans-Peter Seidel,et al.  Perceptual evaluation of tone mapping operators with real-world scenes , 2005, IS&T/SPIE Electronic Imaging.

[28]  Michael Wimmer,et al.  Evaluation of HDR tone mapping methods using essential perceptual attributes , 2008, Comput. Graph..

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

[30]  Takahiro Saito,et al.  Variational Retinex Algorithm with its Application to a High - Quality Chroma Key , 2006, 2006 International Conference on Image Processing.

[31]  Wen-Liang Hwang,et al.  Color image enhancement using retinex with robust envelope , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[32]  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.

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

[34]  Edoardo Provenzi,et al.  An Analysis of Visual Adaptation and Contrast Perception for Tone Mapping , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[35]  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.

[36]  Erik Reinhard,et al.  High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting , 2010 .

[37]  Greg Turk,et al.  LCIS: a boundary hierarchy for detail-preserving contrast reduction , 1999, SIGGRAPH.

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

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