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The lack of large-scale noisy-clean image pairs restricts superviseddenoising methods' deployment in actual applications. While existingunsupervised methods are able to learn image denoising without ground-truthclean images, they either show poor performance or work under impracticalsettings (e.g., paired noisy images). In this paper, we present a practicalunsupervised image denoising method to achieve state-of-the-art denoisingperformance. Our method only requires single noisy images and a noise model,which is easily accessible in practical raw image denoising. It performs twosteps iteratively: (1) Constructing a noisier-noisy dataset with random noisefrom the noise model; (2) training a model on the noisier-noisy dataset andusing the trained model to refine noisy images to obtain the targets used inthe next round. We further approximate our full iterative method with a fastalgorithm for more efficient training while keeping its original highperformance. Experiments on real-world, synthetic, and correlated noise showthat our proposed unsupervised denoising approach has superior performancesover existing unsupervised methods and competitive performance with supervisedmethods. In addition, we argue that existing denoising datasets are of lowquality and contain only a small number of scenes. To evaluate raw imagedenoising performance in real-world applications, we build a high-quality rawimage dataset SenseNoise-500 that contains 500 real-life scenes. The datasetcan serve as a strong benchmark for better evaluating raw image denoising. Codeand dataset will be released at https://github.com/zhangyi-3/IDR

IDR: Self-Supervised Image Denoising via Iterative Data Refinement