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
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
method 1
citation-polarity summary
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2roles
method 1polarities
background 1representative citing papers
FlowSR reformulates SR as a rectified flow and applies consistency distillation with HR regularization plus fast-slow scheduling to enable single-step high-quality super-resolution.
citing papers explorer
-
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.
-
Fast Image Super-Resolution via Consistency Rectified Flow
FlowSR reformulates SR as a rectified flow and applies consistency distillation with HR regularization plus fast-slow scheduling to enable single-step high-quality super-resolution.