AI RESEARCH
Disentangled Textual Priors for Diffusion-based Image Super-Resolution
arXiv CS.CV
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ArXi:2603.07430v1 Announce Type: new Image Super-Resolution (SR) aims to reconstruct high-resolution images from degraded low-resolution inputs. While diffusion-based SR methods offer powerful generative capabilities, their performance heavily depends on how semantic priors are structured and integrated into the generation process. Existing approaches often rely on entangled or coarse-grained priors that mix global layout with local details, or conflate structural and textural cues, thereby limiting semantic controllability and interpretability.