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arXiv · 2605.14677

An Attention-Enhanced Network with Joint Dehazing and Retinex-Based Enhancement for Underwater Images

Sahana Ray·Bibhabasu Debnath·Sanjay Ghosh
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Abstract

Underwater images suffer from severe wavelength-dependent light absorption and scattering, and turbidity due to suspended particles, degrading visual quality for applications in autonomous underwater vehicles (AUVs), marine biology, archaeology, and offshore infrastructure inspection. Classical IFM inadequately capture nonlinear underwater light behavior, while purely data-driven methods lack physical interpretability. This paper proposes a three-stage network named ADR, that extends the underwater image formation model with additional terms to perform underwater dehazing, followed by Retinex-based enhancement and attention-enabled U-Net++ refinement. Experiments on UIEB and UFO-120 benchmark datasets demonstrate competitive performance with state-of-the-art methods.

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Sahana Ray, Bibhabasu Debnath, Sanjay Ghosh. 2026-05-14. An Attention-Enhanced Network with Joint Dehazing and Retinex-Based Enhancement for Underwater Images. https://arxiv.org/abs/2605.14677

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