AI RESEARCH
Learning to Recorrupt: Noise Distribution Agnostic Self-Supervised Image Denoising
arXiv CS.CV
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ArXi:2603.25869v1 Announce Type: cross Self-supervised image denoising methods have traditionally relied on either architectural constraints or specialized loss functions that require prior knowledge of the noise distribution to avoid the trivial identity mapping. Among these, approaches such as Noisier2Noise or Recorrupted2Recorrupted, create