Decoupling Cross-Modality Manifold Discrepancy: Leveraging Visible Diffusion Priors for Infrared Super-Resolution
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TL;DR - Shift-IISR is a dual-path diffusion framework that adapts visible-image diffusion priors for infrared image super-resolution. It aims to improve global distribution and local structural consistency without sacrificing diffusion models’ generative capacity.
- Global Representation Modulation extracts infrared-specific information to steer outputs toward the ground-truth distribution.
- Local Structure Refinement emphasizes structural details throughout iterative denoising.
- Experiments report improved distributional and structural consistency with competitive super-resolution performance.
- The authors provide a public code repository.
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Decoupling Cross-Modality Manifold Discrepancy: Leveraging Visible Diffusion Priors for Infrared Super-Resolution
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TL;DR - Shift-IISR is a dual-path diffusion framework that adapts visible-image diffusion priors for infrared image super-resolution. It aims to improve global distribution and local structural consistency without sacrificing diffusion models’ generative capacity.
- Global Representation Modulation extracts infrared-specific information to steer outputs toward the ground-truth distribution.
- Local Structure Refinement emphasizes structural details throughout iterative denoising.
- Experiments report improved distributional and structural consistency with competitive super-resolution performance.
- The authors provide a public code repository.