SEGD decouples infrared degradations via degradation-specific residual modules, an evidential perception network, and structural-entropy path selection to surpass prior all-in-one methods with fewer parameters.
Prores: Exploring degradation-aware visual prompt for universal image restora- tion
3 Pith papers cite this work. Polarity classification is still indexing.
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ECMRNet is a continual-learning restoration network that decomposes features into isolated groups, expands new groups for novel degradations, prunes via structural entropy, and mines historical components for compound degradations in open-world TIR imaging.
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.
citing papers explorer
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Breaking Degradation Coupling: A Structural Entropy Guided Decoupled Framework and Benchmark for Infrared Enhancement
SEGD decouples infrared degradations via degradation-specific residual modules, an evidential perception network, and structural-entropy path selection to surpass prior all-in-one methods with fewer parameters.
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Expandable, Compressible, Mineable: Open-World Thermal Image Restoration
ECMRNet is a continual-learning restoration network that decomposes features into isolated groups, expands new groups for novel degradations, prunes via structural entropy, and mines historical components for compound degradations in open-world TIR imaging.
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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.