文献类型：期刊文献

英文题名：Comparative Analysis of Traditional and Deep Learning Approaches for Underwater Remote Sensing Image Enhancement: A Quantitative Study

作者：Ma, Yunsheng[1,2];Cheng, Yanan[3];Zhang, Dapeng[1]

机构：[1]Guangdong Ocean Univ, Ship & Maritime Coll, Zhanjiang 524005, Peoples R China;[2]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang 524088, Peoples R China;[3]NJUST, Taizhou Inst Sci & Technol, Coll Business, Taizhou 225300, Peoples R China

年份：2025

卷号：13

期号：5

外文期刊名：JOURNAL OF MARINE SCIENCE AND ENGINEERING

收录：SCI-EXPANDED(收录号：WOS:001497514000001)、、EI(收录号：20252218521762)、Scopus(收录号：2-s2.0-105006780522)、WOS

语种：英文

外文关键词：underwater remote sensing; deep learning algorithms; multi-scale fusion-enhanced physical model; underwater image enhancement

外文摘要：Underwater remote sensing image enhancement is complicated by low illumination, color bias, and blurriness, affecting deep-sea monitoring and marine resource development. This study compares a multi-scale fusion-enhanced physical model and deep learning algorithms to optimize intelligent processing. The physical model, based on the Jaffe-McGlamery model, integrates multi-scale histogram equalization, wavelength compensation, and Laplacian sharpening, using cluster analysis to target enhancements. It performs well in shallow, stable waters (turbidity < 20 NTU, depth < 10 m, PSNR = 12.2) but struggles in complex environments (turbidity > 30 NTU). Deep learning models, including water-net, UWCNN, UWCycleGAN, and U-shape Transformer, excel in dynamic conditions, achieving UIQM = 0.24, though requiring GPU support for real-time use. Evaluated on the UIEB dataset (890 images), the physical model suits specific scenarios, while deep learning adapts better to variable underwater settings. These findings offer a theoretical and technical basis for underwater image enhancement and support sustainable marine resource use.