RUIESR: Realistic Underwater Image Enhancement and Super Resolution

Clear and high-resolution (HR) underwater images are indispensable in acquiring underwater information. However, existing underwater image enhancement and super-resolution (UIESR) networks achieve limited enhancement-super-resolution performance on real-world turbid low-resolution (LR) underwater im...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems for video technology 2024-06, Vol.34 (6), p.4713-4728
Main Authors: Li, Yinyi, Shen, Liquan, Li, Mengyao, Wang, Zhengyong, Zhuang, Lihao
Format: Article
Language:English
Subjects:
Blurring
Color
color casts
Degradation
Distortion
Image color analysis
Image contrast
Image enhancement
Image processing
Image reconstruction
Image resolution
Learning
Modules
resolution degradation
Superresolution
Task analysis
Underwater
underwater degradation
Underwater image enhancement and super-resolution
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Summary:Clear and high-resolution (HR) underwater images are indispensable in acquiring underwater information. However, existing underwater image enhancement and super-resolution (UIESR) networks achieve limited enhancement-super-resolution performance on real-world turbid low-resolution (LR) underwater images because 1) they assume that the resolution degradation is simple and known bicubic down-sampling, generating unrealistic training data for UIESR task; 2) they extract known priors from the underwater imaging model, which is meager to address complex UIESR problems caused by unknown mixed dual-degradation; and 3) they ignore the interaction between blurring and color casts in the RGB color space, leading to unsatisfactory correction results of two distortions. To address these issues, we propose a realistic UIESR network (RUIESR) consisting of three parts: a realistic LR image generation module (RLGM), a dual-degradation estimation module (DEM), and an enhancement and super-resolution module (ESRM). Firstly, RLGM aims to generate LR images obeying underwater LR image distribution by learning real LR properties from unpaired real LR-HR underwater images for training. Secondly, a contrast-driven learning strategy is proposed in the DEM to accurately estimate unknown dual-degradation priors that can aid the reconstruction task. Finally, ESRM is proposed to enhance textures and correct color casts, which includes a dual-branch structure to separate blurring and color casts distortions and utilizes specific priors for each distortion to assist reconstruction. Extensive experiments on real and synthetic underwater datasets show that the proposed RUIESR outperforms existing works regarding visual quality and quantitative metrics.
ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2023.3328785