Issues About Retinex Theory and Contrast Enhancement
International Journal of Computer Vision
HVS-aware ROI-Based illumination and color restoration
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Perceptually motivated automatic color contrast enhancement based on color constancy estimation
Journal on Image and Video Processing - Special issue on emerging methods for color image and video quality enhancement
Toggle and top-hat based morphological contrast operators
Computers and Electrical Engineering
A linear system form solution to compute the local space average color
Machine Vision and Applications
Multi-objective optimization based color constancy
Applied Soft Computing
A Wavelet Perspective on Variational Perceptually-Inspired Color Enhancement
International Journal of Computer Vision
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Starting from the revolutionary Retinex by Land and McCann, several further perceptually inspired color correction models have been developed with different aims, e.g. reproduction of color sensation, robust features recognition, enhancement of color images. Such models have a differential, spatially-variant and non-linear nature and they can coarsely be distinguished between white-patch (WP) and gray-world (GW) algorithms. In this paper we show that the combination of a pure WP algorithm (RSR: Random Spray Retinex) and an essentially GW one (ACE) leads to a more robust and better performing model (RACE). The choice of RSR and ACE follows from the recent identification of a unified spatially-variant approach for both algorithms. Mathematically, the originally distinct non-linear and differential mechanisms of RSR and ACE have been fused using the spray technique and local average operations. The investigation of RACE allowed us to put in evidence a common drawback of differential models: corruption of uniform image areas. To overcome this intrinsic defect, we devised a local and global contrast-based and image-driven regulation mechanism that has a general applicability to perceptually inspired color correction algorithms. Tests, comparisons and discussions are presented.