AI RESEARCH
IR-Flow: Bridging Discriminative and Generative Image Restoration via Rectified Flow
arXiv CS.CV
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ArXi:2604.19680v1 Announce Type: new In image restoration, single-step discriminative mappings often lack fine details via expectation learning, whereas generative paradigms suffer from inefficient multi-step sampling and noise-residual coupling. To address this dilemma, we propose IR-Flow, a novel image restoration method based on Rectified Flow that serves as a unified framework bridging the gap between discriminative and generative paradigms.