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.
Deep multi-scale convolutional neural network for dynamic scene deblurring
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BIR-Adapter adds a parameter-efficient attention adapter and guided sampling to pretrained diffusion models, achieving competitive blind image restoration performance with up to 36x fewer trained parameters and enabling extension to new degradation types.
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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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BIR-Adapter: A parameter-efficient diffusion adapter for blind image restoration
BIR-Adapter adds a parameter-efficient attention adapter and guided sampling to pretrained diffusion models, achieving competitive blind image restoration performance with up to 36x fewer trained parameters and enabling extension to new degradation types.