STN: Stochastic Triplet Neighboring Approach to Self-supervised Denoising from Limited Noisy Images

Bowen Wan, Daming Shi, Yukun Liu

Abstract: With the rapid development of artificial intelligence in recent years, deep learning has shown great potential in the field of image denoising. However, most of the work is based on supervised learning, and the lack of clean images in the real application will limit neural network training. For this reason, self-supervised learning in the absence of clean images is getting more and more attentions. Nevertheless, since both the source and target in self-supervised training are the limited noisy image itself, such denoising methods suffer from overfitting. To this end, a stochastic triplet neighboring approach, thereafter referred to as STN, is proposed in this paper. Given an input noisy image, the source fed to the STN is the downsized sub-image via 4-neighbor sampling, whereas the target in STN training is a stochastic combination from the two neighbored sub-images. Such a mechanism is actually the augmentation of training data, which leads to the relief of the overfitting problem. Extensive experimental results show that our proposed STN approach outperforms the state-of-the-art image denoising methods.