Retinex at 50: color theory and spatial algorithms, a review

John J. McCann

doi:10.1117/1.JEI.26.3.031204

17 February 2017 Retinex at 50: color theory and spatial algorithms, a review

John J. McCann

Author Affiliations +

Journal of Electronic Imaging, Vol. 26, Issue 3, 031204 (February 2017). https://doi.org/10.1117/1.JEI.26.3.031204

Abstract

Retinex Imaging shares two distinct elements: first, a model of human color vision; second, a spatial-imaging algorithm for making better reproductions. Edwin Land’s 1964 Retinex Color Theory began as a model of human color vision of real complex scenes. He designed many experiments, such as Color Mondrians, to understand why retinal cone quanta catch fails to predict color constancy. Land’s Retinex model used three spatial channels (L, M, S) that calculated three independent sets of monochromatic lightnesses. Land and McCann’s lightness model used spatial comparisons followed by spatial integration across the scene. The parameters of their model were derived from extensive observer data. This work was the beginning of the second Retinex element, namely, using models of spatial vision to guide image reproduction algorithms. Today, there are many different Retinex algorithms. This special section, “Retinex at 50,” describes a wide variety of them, along with their different goals, and ground truths used to measure their success. This paper reviews (and provides links to) the original Retinex experiments and image-processing implementations. Observer matches (measuring appearances) have extended our understanding of how human spatial vision works. This paper describes a collection very challenging datasets, accumulated by Land and McCann, for testing algorithms that predict appearance.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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John J. McCann "Retinex at 50: color theory and spatial algorithms, a review," Journal of Electronic Imaging 26(3), 031204 (17 February 2017). https://doi.org/10.1117/1.JEI.26.3.031204

Received: 7 November 2016; Accepted: 12 January 2017; Published: 17 February 2017

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