Stochastic HKMDHE: A multi-objective contrast enhancement algorithm
Sawon Pratiher, Sabyasachi Mukhopadhyay, Srideep Maity, Prasanta Kumar Panigrahi, Asima Pradhan, Nirmalya Ghosh · 2018
This contribution proposes a novel extension of the existing ‘Hyper Kurtosis based Modified Duo-Histogram Equalization’ (HKMDHE) algorithm, for multi-objective contrast enhancement of biomedical images. A novel modified objective function has been formulated by joint optimization of the individual histogram equalization objectives. The optimal adequacy of the proposed methodology with respect to image quality metrics such as brightness preserving abilities, peak signal-to-noise ratio (PSNR), Structural Similarity Index (SSIM) and universal image quality metric has been experimentally validated. The performance analysis of the proposed Stochastic HKMDHE with existing histogram equalization methodologies like Global Histogram Equalization (GHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) has been given for comparative evaluation.