{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T07:07:12Z","timestamp":1773731232478,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T00:00:00Z","timestamp":1744156800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ningbo Major Research and Development Plan Project","award":["2024Z114"],"award-info":[{"award-number":["2024Z114"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Underwater image enhancement (UIE) is inherently challenging due to complex degradation effects such as light absorption and scattering, which result in color distortion and a loss of fine details. Most existing methods focus on spatial-domain processing, often neglecting the frequency-domain characteristics that are crucial for effectively restoring textures and edges. In this paper, we propose a novel UIE framework, the Wavelet-based Enhancement Diffusion Model (WEDM), which integrates frequency-domain decomposition with diffusion models. The WEDM consists of two main modules: the Wavelet Color Compensation Module (WCCM) for color correction in the LAB space using discrete wavelet transform, and the Wavelet Diffusion Module (WDM), which replaces traditional convolutions with wavelet-based operations to preserve multi-scale frequency features. By combining residual denoising diffusion with frequency-specific processing, the WEDM effectively reduces noise amplification and high-frequency blurring. Ablation studies further demonstrate the essential roles of the WCCM and WDM in improving color fidelity and texture details. Our framework offers a robust solution for underwater visual tasks, with promising applications in marine exploration and ecological monitoring.<\/jats:p>","DOI":"10.3390\/jimaging11040114","type":"journal-article","created":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T03:32:53Z","timestamp":1744169573000},"page":"114","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["WEDM: Wavelet-Enhanced Diffusion with Multi-Stage Frequency Learning for Underwater Image Enhancement"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-5242-9469","authenticated-orcid":false,"given":"Junhao","family":"Chen","sequence":"first","affiliation":[{"name":"Faculty of Mechanical Engineering & Mechanics, Ningbo University, Ningbo 315211, China"}]},{"given":"Sichao","family":"Ye","sequence":"additional","affiliation":[{"name":"Ningbo Institute of Materials Technology & Engineering, Chinese Academy of Sciences, Ningbo 315201, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0772-5424","authenticated-orcid":false,"given":"Xiong","family":"Ouyang","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8350-6116","authenticated-orcid":false,"given":"Jiayan","family":"Zhuang","sequence":"additional","affiliation":[{"name":"Ningbo Institute of Materials Technology & Engineering, Chinese Academy of Sciences, Ningbo 315201, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,9]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"YOLOTrashCan: A deep learning marine debris detection network","volume":"72","author":"Zhou","year":"2023","journal-title":"IEEE Trans. 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