A degradation-aware diffusion framework fuses multimodal images under arbitrary degradations by directly regressing the fused image and applying joint degradation-fusion constraints during limited-step sampling.
Contourlet residual for prompt learning enhanced infrared image super-resolution
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 2years
2026 2verdicts
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CLDyN establishes a closed-loop semantic transmission chain with a Requirement-driven Semantic Compensation module to make infrared-visible fusion adapt to diverse downstream tasks.
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Degradation-Robust Fusion: An Efficient Degradation-Aware Diffusion Framework for Multimodal Image Fusion in Arbitrary Degradation Scenarios
A degradation-aware diffusion framework fuses multimodal images under arbitrary degradations by directly regressing the fused image and applying joint degradation-fusion constraints during limited-step sampling.
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Customized Fusion: A Closed-Loop Dynamic Network for Adaptive Multi-Task-Aware Infrared-Visible Image Fusion
CLDyN establishes a closed-loop semantic transmission chain with a Requirement-driven Semantic Compensation module to make infrared-visible fusion adapt to diverse downstream tasks.