In the last few years, image denoising has benefited a lot from the fast
development of neural networks. However, the requirement of large amounts of
noisy-clean image pairs for supervision limits the wide use of these models.
Although there have been a few attempts in training an image denoising model
with only single noisy images, existing self-supervised denoising approaches
suffer from inefficient network training, loss of useful information, or
dependence on noise modeling. In this paper, we present a very simple yet
effective method named Neighbor2Neighbor to train an effective image denoising
model with only noisy images. Firstly, a random neighbor sub-sampler is
proposed for the generation of training image pairs. In detail, input and
target used to train a network are images sub-sampled from the same noisy
image, satisfying the requirement that paired pixels of paired images are
neighbors and have very similar appearance with each other. Secondly, a
denoising network is trained on sub-sampled training pairs generated in the
first stage, with a proposed regularizer as additional loss for better
performance. The proposed Neighbor2Neighbor framework is able to enjoy the
progress of state-of-the-art supervised denoising networks in network
architecture design. Moreover, it avoids heavy dependence on the assumption of
the noise distribution. We explain our approach from a theoretical perspective
and further validate it through extensive experiments, including synthetic
experiments with different noise distributions in sRGB space and real-world
experiments on a denoising benchmark dataset in raw-RGB space.

本文提出了一种名为 Neighbor2Neighbor 的简单而有效的方法，利用自监督学习中的随机邻居子采样器在只有噪声图像的情况下训练图像降噪模型，并在理论和实验验证中证明了其有效性。