Contrast enhancement using entropy-based dynamic sub-histogram equalisation
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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.