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

Sahana Ray, Bibhabasu Debnath, Sanjay Ghosh
May 14, 2026
Published Date

Research Abstract & Technology Focus

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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Frequently Asked Questions (FAQ)

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What is the core focus of the research titled 'An Attention-Enhanced Network with Joint Dehazing and Retinex-Based Enhancement for Underwater Images'?

This literature focuses on: 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, a...

What other academic literature is closely related to 'An Attention-Enhanced Network with Joint Dehazing and Retinex-Based Enhancement for Underwater Images'?

Yes, highly correlated activity was mapped. An entry titled 'Physical prior-guided SAM adaptation for underwater scene segmentation' discusses this: Underwater image segmentation is fundamental to marine exploration and autonomous underwater vehicle navigation, yet its accuracy is severely compr...

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