Optimized multi-scale framework for image enhancement using spatial information-based histogram equalization

The histogram equalization method, used to improve images, minimizes the amount of pixel intensities, which results in the loss of detail and an unnatural appearance. This study presents an approach for enhancing low contrast images based on their inherent characteristics. The statistical parameter skewness is derived from the photographs to facilitate the classification process into dark and bright images. Based on the classification, appropriate dark and bright pass filters are applied on the multi-level decomposed images to extract the significant edge details. The level of decomposition is optimized using particle swarm optimization. The extracted edge details are utilized by the two-dimensional histogram equalization technique. It leverages the combined presence of edge information and pixel intensities in the low contrast image. The algorithm's efficacy is assessed on three databases, namely CCID, LOL, and DRESDEN, through the utilization of standard deviation (SD), contrast improvement index (CII), discrete entropy (DE), the natural image quality evaluator (NIQE), and Kullback-Leibler distance (KL). Based on the empirical findings, it can be observed that the suggested methodology exhibits better performance compared to alternative methods, including deep learning architectures, in terms of high CII, SD, DE, and low NIQE, KL values.