TexADiff integrates a Relative Texture Density Map into diffusion-based super-resolution to address imbalanced textures in remote sensing images, yielding better high-frequency details and downstream task gains.
Designing a practical degradation model for deep blind image super-resolution
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FlowSR enables single-step image super-resolution by learning a rectified flow from LR to HR with consistency distillation, HR regularization, and dual fast-slow timestep scheduling.
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Remote Sensing Image Super-Resolution for Imbalanced Textures: A Texture-Aware Diffusion Framework
TexADiff integrates a Relative Texture Density Map into diffusion-based super-resolution to address imbalanced textures in remote sensing images, yielding better high-frequency details and downstream task gains.
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Fast Image Super-Resolution via Consistency Rectified Flow
FlowSR enables single-step image super-resolution by learning a rectified flow from LR to HR with consistency distillation, HR regularization, and dual fast-slow timestep scheduling.