Abstract: Images captured underwater often exhibit low contrast and color distortion attributed to special properties of light in water. Underwater image enhancement methods have become an effective solution to address these issues due to its simplicity and effectiveness. However, underwater image enhancement methods (such as CLAHE) face challenge of increasing image contrast, improve generalization of method. Here, underwater image enhancement via higher-order moment CLAHE model and V channel substitute is proposed to enhance contrast and correct color distortion. Firstly, analyze statistical features of image histograms, use higher-order moments to quantify features in a targeted manner, add them to CLAHE, so that improved CLAHE can accurately enhance contrast of underwater image according to dynamic features of image blocks, avoiding over- or under-enhancement of image. Then, for problem of color distortion, this paper novelty uses gray data to substitutes V channel in HSV color space, compensated for lost information, so as to achieve purpose of color correction in terms of visual perception. Finally, color correction of image through gray world method, which effectively improve color distortion problem. Our method is qualitatively and quantitatively compared with multiple state-of-the-art methods in public dataset, demonstrating that this method better solved low contrast and color distortion, in addition, details were more realistic, and evaluation indexes of underwater image quality were better.

Chen Yahui, Liang Yitao, Li Yongfeng, Liu Hongyue and Li Lan, “Underwater Image Enhancement via Higher-Order Moment CLAHE Model and V Channel Substitute” International Journal of Advanced Computer Science and Applications(IJACSA), 15(3), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150320

@article{Yahui2024,
title = {Underwater Image Enhancement via Higher-Order Moment CLAHE Model and V Channel Substitute},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150320},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150320},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {3},
author = {Chen Yahui and Liang Yitao and Li Yongfeng and Liu Hongyue and Li Lan}
}