Learning to Recorrupt: Noise Distribution Agnostic Self-Supervised Image Denoising
Self-supervised image denoising methods have traditionally relied on architectural constraints, pseudo-pair constructions, or specialized loss functions to avoid the trivial identity mapping. Among these, approaches such as Noisier2Noise or R2R create training pairs by adding synthetic noise to noisy images. While effective, these recorruption-based approaches require precise knowledge of the noise distribution, which is often unavailable. We present Learning to Recorrupt (L2R), a self-supervised framework that does not require exact knowledge of the noise distribution. Our method introduces a learnable recorruptor jointly optimized with the denoiser through a min--max saddle-point object...
| Authors | Brayan Monroy, Jorge Bacca, Julián Tachella |
|---|---|
| Primary category | eess.IV |
| Published | 26 March 2026 |
| Last updated at source | 28 September 2026 |
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