To address the technical shortcomings of conventional histogram equalization (HE), such as over-enhancement and artifacts, we propose a histogram-constrained contrast-tunable HE technique for digital image enhancement. Firstly, input is partitioned into two parts, main constrained histogram, by cumulative probability density threshold; second, redistributed equally in whole grayscale range; fin...

in this paper, the performance of 11 different distances for image retrieval and classification, based on color, shape and texture, is evaluated. the precision-recall measure and the correct classification rate of the k-nn classifier are used to evaluate retrieval and classification performances, respectively. the experimental results for a database of 1000 images from 10 different semantic gro...

– Histogram equalization – Preserving the original brightness to avoid annoying artifacts ‹ Bright preserving Bi-Histogram Equalization – Not handling well to avoid annoying artifacts ‹ Recursive Mean-Separate Histogram Equalization – Separating each new histogram further based on respective mean Abstract 3 / 28 ‹ Histogram equalization – Base on the probability distribution of the input gray l...