Contrast enhancement using entropy‐based dynamic sub‐histogram equalisation. Issue 11 (1st November 2016) - Parihar, Anil Singh
Abstract : This study presents a new contrast‐enhancement approach called entropy‐based dynamic sub‐histogram equalisation. The proposed algorithm performs a recursive division of the histogram based on the entropy of the sub‐histograms. Each sub‐histogram is divided recursively into two sub‐histograms with equal entropy. A stopping criterion is proposed to achieve an optimum number of sub‐histograms. A new dynamic range is allocated to each sub‐histogram based on the entropy and number of used and missing intensity levels in the sub‐histogram. The final contrast‐enhanced image is obtained by equalising each sub‐histogram independently. The proposed algorithm is compared with conventional as well as state‐of‐the‐art contrast‐enhancement algorithms. The quantitative results for a large image data set are statistically analysed using a paired t ‐test. The quantitative and visual assessment shows that the proposed algorithm outperforms most of the existing contrast‐enhancement algorithms. The proposed algorithm results in natural‐looking, good contrast images with almost no artefacts.