Survey of methods and evaluation of Retinex-inspired image enhancers

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. (c) 2022 SPIE and IS&T

Automated summary

This paper reviews 35 different Retinex-inspired spatial color algorithms and provides a set of metrics for evaluating their image quality. The paper also introduces a real-world color image dataset called SCA-30 and makes the data and code used for evaluation freely available for further analysis and comparisons.