Spatial color algorithms (SCAs) are computer vision procedures widely used for image enhancement and human vision modeling. The main characteristic of SCA family is that they mimic the behavior of the human vision system (HVS), achieving in this way robustness and the capability to adjust their effect according to the image content. Here, we review 35 different, popular Retinex-inspired SCAs discussing and providing a set of measures for their evaluation in terms of image quality. To this purpose, we also introduce SCA-30, a real-world color image dataset made publicly available. The algorithms considered here include and spread from well-known Retinex implementations, Retinex variants, Milano–Retinex and related inspired enhancers, illumination/decomposition approaches, and deep learning-based techniques. Data and code used for the evaluation are made freely available to the community, to pursue further analysis and comparisons.
Glare is always present in optical acquisition systems, such as photocameras or the eye-bulb. As a consequence, images captured by sensors do not represent an accurate reproduction of the scene, but rather a combination of scene content and glare. We discuss the reasons why this unwanted addition of spread light cannot be removed from an acquired image. To this aim, we cast the problem of glare-removal into an estimation task and focus on the aspects that make the unfolding of glare an ill-posed or ill-conditioned problem—such as nonlinearity, information loss, or eye model uncertainty. For each mechanism of glare formation, we point to the corresponding influence in terms of ill-posedness and ill-conditioning of the problem. We do not aim at proposing or reviewing solutions to the glare problem but rather at identifying more precisely the challenges it poses.
Some spatial color algorithms, such as Brownian Milano retinex (MI-retinex) and random spray retinex (RSR), are based on sampling. In Brownian MI-retinex, memoryless random walks (MRWs) explore the neighborhood of a pixel and are then used to compute its output. Considering the relative redundancy and inefficiency of MRW exploration, the algorithm RSR replaced the walks by samples of points (the sprays). Recent works point to the fact that a mapping from the sampling formulation to the probabilistic formulation of the corresponding sampling process can offer useful insights into the models, at the same time featuring intrinsically noise-free outputs. The paper continues the development of this concept and shows that the population-based versions of RSR and Brownian MI-retinex can be used to obtain analytical expressions for the outputs of some test images. The comparison of the two analytic expressions from RSR and from Brownian MI-retinex demonstrates not only that the two outputs are, in general, different but also that they depend in a qualitatively different way upon the features of the image.
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