Controlling Re-corruption Bias for Unsupervised Image Denoising under Unknown Noise

Unsupervised image denoising via re-corruption constructs noisy/noisy training pairs whose loss can reproduce supervised denoising under suitable noise assumptions. Existing guarantees, however, require sufficiently specified noise statistics, which are often unavailable in practice. We revisit re-corruption and identify a denoiser-independent criterion that characterizes the bias induced by a given re-corruption. This criterion depends only on a conditional moment of the re-corruption mismatch, yielding a weaker requirement than exact conditional noise matching and an equivalent variational formulation for learning re-corruption from noisy data alone. Based on this characterization, we first learn the re-corruption mechanism and then freeze it to fine-tune an initial denoiser, thereby decoupling re-corruption learning from denoiser optimization. By targeting the bias-relevant mismatch rather than estimating the full noise distribution, our method naturally accommodates unknown real-world noise. Experiments on smartphone and fluorescence-microscopy images show consistent denoising improvements and a clear reduction of the predicted bias.