DS-DiT decouples low-resolution and reference interactions in a siamese diffusion transformer and adds a patch-level weights module plus autoguidance to improve reference-based super-resolution for remote sensing images.
IEEE transactions on pattern analysis and ma- chine intelligence45(4), 4713–4726 (2022)
3 Pith papers cite this work. Polarity classification is still indexing.
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TOC-SR builds a compact one-step diffusion model for image super-resolution achieving 6.6x fewer parameters and 2.8x fewer GMACs while maintaining strong reconstruction quality.
SUMI distills photon-counting CT quality into routine chest CT by learning to reverse clinically validated acquisition degradations, yielding 15-20% gains in image metrics, better radiologist utility, and up to 15% higher lesion detection sensitivity.
citing papers explorer
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Learning to Balance: Decoupled Siamese Diffusion Transformer for Reference-Based Remote Sensing Image Super-Resolution
DS-DiT decouples low-resolution and reference interactions in a siamese diffusion transformer and adds a patch-level weights module plus autoguidance to improve reference-based super-resolution for remote sensing images.
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TOC-SR: Task-Optimal Compact diffusion for Image Super Resolution
TOC-SR builds a compact one-step diffusion model for image super-resolution achieving 6.6x fewer parameters and 2.8x fewer GMACs while maintaining strong reconstruction quality.
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Distilling Photon-Counting CT into Routine Chest CT through Clinically Validated Degradation Modeling
SUMI distills photon-counting CT quality into routine chest CT by learning to reverse clinically validated acquisition degradations, yielding 15-20% gains in image metrics, better radiologist utility, and up to 15% higher lesion detection sensitivity.