On edge-aware path-based color spatial sampling for Retinex: from Termite Retinex to Light Energy-driven Termite Retinex

Abstract. Retinex theory estimates the human color sensation at any observed point by correcting its color based on the spatial arrangement of the colors in proximate regions. We revise two recent path-based, edge-aware Retinex implementations: Termite Retinex (TR) and Energy-driven Termite Retinex (ETR). As the original Retinex implementation, TR and ETR scan the neighborhood of any image pixel by paths and rescale their chromatic intensities by intensity levels computed by reworking the colors of the pixels on the paths. Our interest in TR and ETR is due to their unique, content-based scanning scheme, which uses the image edges to define the paths and exploits a swarm intelligence model for guiding the spatial exploration of the image. The exploration scheme of ETR has been showed to be particularly effective: its paths are local minima of an energy functional, designed to favor the sampling of image pixels highly relevant to color sensation. Nevertheless, since its computational complexity makes ETR poorly practicable, here we present a light version of it, named Light Energy-driven TR, and obtained from ETR by implementing a modified, optimized minimization procedure and by exploiting parallel computing.

[1]  Alessandro Rizzi,et al.  Spatio-Temporal Retinex-Inspired Envelope with Stochastic Sampling: A Framework for Spatial Color Algorithms , 2011 .

[2]  Alessandro Rizzi,et al.  On the behavior of spatial models of color , 2007, Electronic Imaging.

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

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

[5]  Alessandro Rizzi,et al.  Tuning the locality of filtering with a spatially weighted implementation of random spray Retinex. , 2015, Journal of the Optical Society of America. A, Optics, image science, and vision.

[6]  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..

[7]  J. Albers,et al.  Interaction of Color , 1971 .

[8]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[9]  Alessandro Rizzi,et al.  Energy-driven path search for Termite Retinex. , 2016, Journal of the Optical Society of America. A, Optics, image science, and vision.

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

[11]  Ivar Farup,et al.  Spatio-Temporal Retinex-like Envelope with Total Variation , 2012, CGIV.

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

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

[14]  Corso Elvezia,et al.  Ant colonies for the traveling salesman problem , 1997 .

[15]  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.

[16]  Éva Tardos,et al.  Algorithm design , 2005 .

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

[18]  Matti Pietikäinen,et al.  Outex - new framework for empirical evaluation of texture analysis algorithms , 2002, Object recognition supported by user interaction for service robots.

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

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

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

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

[23]  Carlo Gatta,et al.  A new algorithm for unsupervised global and local color correction , 2003, Pattern Recognit. Lett..

[24]  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.

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

[26]  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.

[27]  Alessandro Rizzi,et al.  Termite Retinex: a new implementation based on a colony of intelligent agents , 2014, J. Electronic Imaging.

[28]  Alessandro Rizzi,et al.  The Art and Science of HDR Imaging: McCann/The Art and Science of HDR Imaging , 2011 .

[29]  Ernesto Damiani,et al.  A Retinex model based on Absorbing Markov Chains , 2016, Inf. Sci..

[30]  Rasmus Larsen,et al.  Contrast Enhancement and Metrics for Biometric Vein Pattern Recognition , 2010, ICIC.

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

[32]  M Dorigo,et al.  Ant colonies for the travelling salesman problem. , 1997, Bio Systems.

[33]  Nikola Banić,et al.  Smart light random memory sprays Retinex: a fast Retinex implementation for high-quality brightness adjustment and color correction. , 2015, Journal of the Optical Society of America. A, Optics, image science, and vision.

[34]  Emmanuelle Gouillart,et al.  scikit-image: image processing in Python , 2014, PeerJ.

[35]  J. Deneubourg,et al.  The self-organizing exploratory pattern of the argentine ant , 1990, Journal of Insect Behavior.

[36]  Luca Fanucci,et al.  Application-Specific Instruction-Set Processor for Retinex-Like Image and Video Processing , 2007, IEEE Transactions on Circuits and Systems II: Express Briefs.

[37]  Alessandro Rizzi,et al.  A proposal for Contrast Measure in Digital Images , 2004, CGIV.

[38]  J. Deneubourg,et al.  Self-organized shortcuts in the Argentine ant , 1989, Naturwissenschaften.

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

[40]  Roberto Montagna,et al.  Constrained pseudo-Brownian motion and its application to image enhancement. , 2011, Journal of the Optical Society of America. A, Optics, image science, and vision.

[41]  Alessandro Rizzi,et al.  QBRIX: a quantile-based approach to retinex. , 2014, Journal of the Optical Society of America. A, Optics, image science, and vision.

[42]  Konstantinos Moutoussis,et al.  The physiology and psychophysics of the color-form relationship: a review , 2015, Front. Psychol..

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

[44]  Alessandro Rizzi,et al.  A population-based approach to point-sampling spatial color algorithms. , 2016, Journal of the Optical Society of America. A, Optics, image science, and vision.

[45]  O. Creutzfeldt,et al.  Darkness induction, retinex and cooperative mechanisms in vision , 2004, Experimental Brain Research.

[46]  Edoardo Provenzi,et al.  A Wavelet Perspective on Variational Perceptually-Inspired Color Enhancement , 2013, International Journal of Computer Vision.