AI RESEARCH
QualiTeacher: Quality-Conditioned Pseudo-Labeling for Real-World Image Restoration
arXiv CS.CV
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ArXi:2603.08030v1 Announce Type: new Real-world image restoration (RWIR) is a highly challenging task due to the absence of clean ground-truth images. Many recent methods resort to pseudo-label (PL) supervision, often within a Mean-Teacher (MT) framework. However, these methods face a critical paradox: unconditionally trusting the often imperfect, low-quality PLs forces the student model to learn undesirable artifacts, while discarding them severely limits data diversity and impairs model generalization.