Research on Histogram Equalization Algorithm Based on Optimized Adaptive Quadruple Segmentation and Cropping of Underwater Image (AQSCHE)

Abstract

Due to the uncertain, diverse, and light-attenuating characteristics of the underwater environment, underwater images have low contrast and unclear problems. This paper proposes a histogram equalization algorithm based on optimized adaptive image quadruple segmentation and cropping (AQSCHE). Compared with the traditional histogram equalization underwater image enhancement algorithm, this algorithm introduces histogram quadruple segmentation and cropping technology. Using the exposure value and segmentation point calculation formula that optimizes the distribution range of the histogram, perform quadruple segmentation on the image to obtain a more refined histogram. The adaptive histogram clipping is realized by constructing the clipping parameter z to adjust the contrast and brightness of the image. The original image is enhanced by double equalization of the sub-histogram and the histogram of each channel. Finally, the simulation experiments verify the enhancement effect of the proposed algorithm AQSCHE on underwater images. The processed underwater image has higher contrast, is clearer and more natural in subjective evaluation, and has a better visual effect; in the image objective evaluation indicators, information entropy (Entropy), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) and universal color image quality evaluator (UCIQE), this algorithm also outperforms other common algorithms such as HE and CLAHE.