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