Underwater image enhancement based on degradation type awareness

Cui Xiaodong; Zhu Qiuwei; Yang Zirui; Fan Miao; Zhu Zhengren; Wang Xiaoming; Yang Fanlin

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Cui Xiaodong，Zhu Qiuwei，Yang Zirui, et al. Underwater image enhancement based on degradation type awareness[J]. Haiyang Xuebao，2026, 48(x)：1–16

Citation:

Cui Xiaodong，Zhu Qiuwei，Yang Zirui, et al. Underwater image enhancement based on degradation type awareness[J]. Haiyang Xuebao，2026, 48(x)：1–16

Citation:

Cui Xiaodong，Zhu Qiuwei，Yang Zirui, et al. Underwater image enhancement based on degradation type awareness[J]. Haiyang Xuebao，2026, 48(x)：1–16

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Underwater image enhancement based on degradation type awareness

1.
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
2.
Ocean Acoustic Technology Laboratory, Institute of Acoustics Chinese Academy of Sciences, Beijing 100190, China
3.
School of Marine Science and Technology, Tianjin University, Tianjin 300072, China
4.
National Marine Data Information Center, Tianjin 300171, China
5.
CCCG Xingu.TechnologyCo.Ltd, Beijing 102299, Chian

Available Online: 2026-02-13

Abstract

Abstract

High-quality underwater optical images are crucial for tasks such as digital twins of seabed scenes, benthic habitat protection, seabed mineral resource detection, and understanding unknown underwater phenomena. However, due to factors such as complex aquatic environments and lighting conditions, underwater optical images suffer from degradation issues including color distortion, blurred details, and low contrast. Existing underwater image enhancement methods often focus on optimizing enhancement algorithms themselves, lacking systematic analytical mechanisms for tracing, classifying, and grading different types of degradation. To address this, considering the complexity and heterogeneity of underwater optical imaging environments, this paper proposes an image quality enhancement strategy that takes degradation types into account. First, a degradation-type-aware network is constructed to identify underwater hazy and blurred images, achieving an accuracy of 97%, and also demonstrating a high distinguishing capability for illumination degradation types. Second, for the identified underwater hazy images, an adaptive color correction method is designed based on the statistical distribution of color bias values in real underwater images, effectively restoring varying degrees of color attenuation. Finally, a block indexing strategy is introduced to obtain more precise background light estimates, further addressing the hazy blur issue in underwater images in conjunction with the underwater dark channel prior. Experimental results on various real underwater image datasets, including UIEB and RUIE, indicate that compared to representative underwater image enhancement methods, the PSNR and SSIM metrics are improved by 22.17% and 4.5%, respectively.
- underwater image enhancement,
- optical imaging,
- degradation perception,
- color correction,
- image dehazing.

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References(39)

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