Zero-Shot Blind-spot Image Denoising via Implicit Neural Sampling

The blind-spot principle has been a widely used tool in zero-shot image denoising but faces challenges with real-world noise that exhibits strong local correlations. Existing methods focus on reducing noise correlation, such as masking or reshuffling, which also weaken the pixel correlations needed for estimating missing pixels, resulting in performance degradation. In this paper, we first present a rigorous analysis of how noise correlation and pixel correlation impact the statistical risk of a linear blind-spot denoiser. We then propose using an implicit neural representation to resample noisy pixels, effectively reducing noise correlation while preserving the essential pixel correlations for successful blind-spot denoising. Extensive experiments show our method surpasses existing zero-shot denoising techniques on real-world noisy images.