Noise2one: One-Shot Image Denoising with Local Implicit Learning

Kwanyoung Kim, Jong Chul Ye

Abstract: Recently, the self-supervised learning paradigm, involving pretraining and fine-tuning large-scale models for downstream tasks, has shown promise in computer vision. Inspired by this, here we introduce Noise2One, a simple and effective image denoising method building upon this paradigm. Noise2One leverages self-supervised learning to pre-train a model on noisy images by learning a score function. Subsequently, fine-tuning on a single clean image enables denoising noisy images. Unlike Noise2Score, which relies on Tweedie’s formula, our method introduces a lightweight decoder based on Local Implicit Image Function (LIIF) for per-pixel noise adjustment and clean image reconstruction. This approach is more versatile, accommodating various noise models, including real-world noise. Our extensive experiments on benchmark datasets demonstrate that Noise2One achieves state-of-the-art denoising performance with only a 2.5% (+0.04M) increase in parameters compared to existing methods.