文献类型：期刊文献

英文题名：Progressive Color Correction and Vision-Inspired Adaptive Framework for Underwater Image Enhancement

作者：Li, Zhenhua[1,2];Liu, Wenjing[1,2];Wang, Ji[1,2];Yang, Yuqiang[1]

机构：[1]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang 524088, Peoples R China;[2]Guangdong Ocean Univ, Guangdong Prov Smart Ocean Sensor Network & Equipm, Zhanjiang 524088, Peoples R China

年份：2025

卷号：13

期号：9

外文期刊名：JOURNAL OF MARINE SCIENCE AND ENGINEERING

收录：SCI-EXPANDED(收录号：WOS:001580299700001)、、EI(收录号：20254119297159)、Scopus(收录号：2-s2.0-105018101696)、WOS

基金：This work was supported in part by the Key Research and Development (R&D) Plan of Shanxi Province (No. 2023-ZDLGY-15), the Guangdong Basic and Applied Basic Research Foundation (No. 2023A1515110770), the Non-funded Science and Technology Research Program of Zhanjiang (No. 2024B01051), and the Program for Scientific Research Start-up Funds of Guangdong Ocean University (Nos. 060302112319 and 060302112309).

语种：英文

外文关键词：underwater image enhancement; vision-inspired image enhancement; image decomposition; color correction

外文摘要：Underwater images frequently exhibit color distortion, detail blurring, and contrast degradation due to absorption and scattering by the underwater medium. This study proposes a progressive color correction strategy integrated with a vision-inspired image enhancement framework to address these issues. Specifically, the progressive color correction process includes adaptive color quantization-based global color correction, followed by guided filter-based local color refinement, aiming to restore accurate colors while enhancing visual perception. Within the vision-inspired enhancement framework, the color-adjusted image is first decomposed into a base layer and a detail layer, corresponding to low- and high-frequency visual information, respectively. Subsequently, detail enhancement and noise suppression are applied in the detail pathway, while global brightness correction is performed in the structural pathway. Finally, results from both pathways are fused to yield the enhanced underwater image. Extensive experiments on four datasets verify that the proposed method effectively handles the aforementioned underwater enhancement challenges and significantly outperforms state-of-the-art techniques.