DVANet proposes a deep unfolding network combining degradation representation with DINOv3 visual priors for unified restoration under complex degradations.
Foundir-v2: Optimizing pre-training data mixtures for image restoration foundation model
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TPGDiff introduces hierarchical triple-prior guidance in a diffusion network, placing degradation priors throughout, structural priors in shallow layers, and semantic priors in deep layers for improved all-in-one image restoration.
The LoViF 2026 Challenge created a benchmark for real-world all-in-one image restoration by evaluating nine submissions across multiple degradation types.
citing papers explorer
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DVANet: Degradation-aware Visual-prior Alignment Network for Image Restoration
DVANet proposes a deep unfolding network combining degradation representation with DINOv3 visual priors for unified restoration under complex degradations.
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TPGDiff: Hierarchical Triple-Prior Guided Diffusion for Image Restoration
TPGDiff introduces hierarchical triple-prior guidance in a diffusion network, placing degradation priors throughout, structural priors in shallow layers, and semantic priors in deep layers for improved all-in-one image restoration.
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LoViF 2026 Challenge on Real-World All-in-One Image Restoration: Methods and Results
The LoViF 2026 Challenge created a benchmark for real-world all-in-one image restoration by evaluating nine submissions across multiple degradation types.