BRIUIE: A Bio-Retina Inspired Underwater Image Enhancement Framework

The dim shooting environment and light scattering and absorption frequently result in degraded underwater images. The images are characterized by uneven brightness, low contrast, color deterioration, and blurred details. Existing underwater image enhancement methods excel in full-reference and non-reference metrics, yet may fail to align with human visual tendencies. To make the restored images more consistent with natural visual effect, an underwater image enhancement framework named BRIUIE is proposed. BRIUIE draws inspiration from the morphology and functions of various cell layers in the vertebrate retina. Following the visual transmission mechanisms of retinal signals, image brightness is balanced by simulating the feedback and dynamic regulation processes of horizontal cells in response to illumination variation. Meanwhile, simulating the center-surround receptive fields of bipolar and ganglion cells and implementing the color opponent mechanism effectively mitigate color distortion and low contrast. The designed multi-scale feature fusion module facilitates the complementary advantage of the ON and OFF visual pathways of ganglion cells, employing a contrastive learning strategy to prevent overfitting because of simple consistency loss. Comprehensive full-/non-reference experiments demonstrate the proposed BRIUIE outperforms other SOTA methods in quantitative evaluations, while also delivering qualitative results that closely align with human visual assessment standards.

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Publication Details

Journal
IEEE Transactions on Circuits and Systems for Video Technology
Published
2025-04-16
DOI
https://doi.org/10.1109/tcsvt.2025.3561601
Citations
1
Primary Topic
Image Enhancement Techniques
Type
article
Field-Weighted Citation Impact
1.28

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article

BRIUIE: A Bio-Retina Inspired Underwater Image Enhancement Framework

Xujie Zheng, Congcong Li, Fei Yuan, Fengqi Xiao
1 citations
IEEE Transactions on Circuits and Systems for Video Technology
Image Enhancement Techniques
1.28
article

BRIUIE: A Bio-Retina Inspired Underwater Image Enhancement Framework

Xujie Zheng, Congcong Li, Fei Yuan, Fengqi Xiao
article en
1 citations

Abstract

The dim shooting environment and light scattering and absorption frequently result in degraded underwater images. The images are characterized by uneven brightness, low contrast, color deterioration, and blurred details. Existing underwater image enhancement methods excel in full-reference and non-reference metrics, yet may fail to align with human visual tendencies. To make the restored images more consistent with natural visual effect, an underwater image enhancement framework named BRIUIE is proposed. BRIUIE draws inspiration from the morphology and functions of various cell layers in the vertebrate retina. Following the visual transmission mechanisms of retinal signals, image brightness is balanced by simulating the feedback and dynamic regulation processes of horizontal cells in response to illumination variation. Meanwhile, simulating the center-surround receptive fields of bipolar and ganglion cells and implementing the color opponent mechanism effectively mitigate color distortion and low contrast. The designed multi-scale feature fusion module facilitates the complementary advantage of the ON and OFF visual pathways of ganglion cells, employing a contrastive learning strategy to prevent overfitting because of simple consistency loss. Comprehensive full-/non-reference experiments demonstrate the proposed BRIUIE outperforms other SOTA methods in quantitative evaluations, while also delivering qualitative results that closely align with human visual assessment standards.

IEEE Transactions on Circuits and Systems for Video TechnologyVol. 35(10)
Xiamen University (CN), University Town of Shenzhen (CN), Xiamen University of Technology (CN), Tsinghua University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 21%
Image Enhancement Techniques
1.28
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BRIUIE: A Bio-Retina Inspired Underwater Image Enhancement Framework — Xujie Zheng, Congcong Li, et al. · IEEE Transactions on Circuits and Systems for Video Technology (2025) | TGRS Research Map | TGRS