A Gradient-based Spatial Color Algorithm for Image Contrast Enhancement

This work presents GREAT-Mix, a novel contrast enhancer derived from the combination of the Retinex-inspired spatial color algorithms STRESS and GREAT. These algorithms improves the visibility of the details and content of any input image by adjusting its colors on the basis of local spatial and visual features processed channel by channel. Precisely, STRESS stretches the intensity I(x) of each pixel x between two bounding values corresponding to the minimum and maximum intensities of sets of pixels randomly sampled with radial density around x, while GREAT re-scales I(x) by a factor obtained by processing a set of edges deterministically selected over the image. GREAT-Mix implements the stretching function of STRESS but determines its bounding values from the edges sampled by GREAT. The result is a new spatial color algorithm that performs contrast enhancement similarly to STRESS, but that, thanks to the deterministic sampling of GREAT, grants robustness to noise and repeatability of the outcomes