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The paper introduces a unified training framework for underwater image enhancement that addresses the scarcity of paired clean-degraded datasets. It employs a text-guided diffusion model to generate diverse, semantically consistent degraded images and uses a progressive degradation curriculum to balance learning across multiple attributes and difficulty levels. The approach also incorporates a transmission-guided one-step diffusion model to handle varying degradation severity, achieving superior performance on several underwater benchmarks.
The paper presents an all-in-one framework for restoring polarization images affected by multiple coupled degradations. It identifies the normalized Stokes representation as effective for separating intensity and polarization, and introduces a dual-branch architecture that uses pretrained general restoration priors for the intensity branch while transferring knowledge to the polarization branch via cross-domain feature transform. A composite-degradation benchmark is also established to support future research.
arXiv:2608. 08965v1 Announce Type: new Abstract: Underwater images often suffer from diverse and coexisting degradations, including color distortion, scattering haze, texture attenuation, and uneven illumination.
UniPolar is a new framework for restoring images and polarimetric data from color polarization filter array (CPFA) images that suffer from both device- and environment-induced degradations. It uses two complementary streams to reconstruct four polarization-angle images and predict Stokes difference components, coordinating them with a Degradation Conditioned Cross Domain Interaction module that exchanges frequency-selective evidence and applies condition-adaptive feature correction. The method also enforces physical consistency at the output level, aligning image-derived and directly predicted Stokes components while regularizing angular fidelity and the physical feasibility of degree of linear polarization (DoLP).
WaterClear-GS introduces a physics-informed Gaussian splatting method tailored for underwater 3D reconstruction and appearance restoration. It models underwater degradation as intrinsic Gaussian attributes and employs a dual-branch optimization that separates clean appearance from degradation while preserving photometric consistency. The approach incorporates depth-guided geometry regularization, perception-driven supervision, exposure constraints, adaptive regularization, and spectral regularization, achieving strong novel view synthesis and image restoration performance at over 160 FPS.
DPSF-Net is a dual‑prior spatial‑frequency network designed for real‑world remote sensing image dehazing. It combines hazy RGB images with dark channel prior maps as joint inputs, and incorporates a spatial‑frequency residual interaction block, a prior‑guided feature attention module, and a selective kernel complementary fusion module to reduce colour shift, structural distortion, and large‑scale haze. Experiments show that DPSF-Net achieves state‑of‑the‑art performance on the RRSHID benchmark while maintaining a favorable balance of restoration quality, parameter count, and computational complexity.