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Zhengyong Wang, Liquan Shen, Mai Xu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|April 7, 2023

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This study introduces a Two-phase Underwater Domain Adaptation network (TUDA) to improve real-world underwater image enhancement. TUDA effectively bridges the gap between synthetic and real data, significantly outperforming existing methods.

Area of Science:

Computer Vision
Image Processing
Deep Learning

Background:

Learning-based algorithms excel at underwater image enhancement but struggle with domain gaps.
Models trained on synthetic data often fail to generalize to real-world underwater images due to inter-domain and intra-domain gaps.
Existing techniques produce artifacts and color distortions on real underwater images.

Purpose of the Study:

To propose a novel Two-phase Underwater Domain Adaptation network (TUDA) to address both inter-domain and intra-domain gaps.
To enhance the generalization capability of underwater image enhancement models for real-world scenarios.
To reduce visually unpleasing artifacts and color distortions in enhanced underwater images.

Main Methods:

TUDA employs a two-phase approach: triple-alignment for inter-domain adaptation and easy-hard adaptation for intra-domain gap reduction.

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Visualizing Visual Adaptation

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Published on: April 24, 2017

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Phase one utilizes adversarial learning for image, feature, and output-level adaptation, bridging the synthetic-real data gap.

Phase two incorporates a rank-based quality assessment and pseudo-labeling for easy-hard adaptation, minimizing real data distribution gaps.

Main Results:

TUDA significantly minimizes both inter-domain and intra-domain gaps in underwater image enhancement.
The proposed rank-based quality assessment accurately evaluates enhanced image perceptual quality.
Extensive experiments confirm TUDA's superiority over existing methods in visual quality and quantitative metrics.

Conclusions:

TUDA offers a robust solution for real-world underwater image enhancement by effectively addressing domain adaptation challenges.
The network demonstrates improved generalization and reduced artifacts compared to current state-of-the-art techniques.
TUDA advances the field by tackling the often-overlooked intra-domain gap in underwater image processing.