Random Sub-Samples Generation for Self-Supervised Real Image Denoising
Yizhong Pan Xiao Liu Xiangyu Liao Yuanzhouhan Cao Chao Ren
With sufficient paired training samples, the supervised deep learning methods
have attracted much attention in image denoising because of their superior
performance. However, it is still very challenging to widely utilize the
supervised methods in real cases due to the lack of paired noisy-clean images.
Meanwhile, most self-supervised denoising methods are ineffective as well when
applied to the real-world denoising tasks because of their strict assumptions
in applications. For example, as a typical method for self-supervised
denoising, the original blind spot network (BSN) assumes that the noise is
pixel-wise independent, which is much different from the real cases. To solve
this problem, we propose a novel self-supervised real image denoising framework
named Sampling Difference As Perturbation (SDAP) based on Random Sub-samples
Generation (RSG) with a cyclic sample difference loss. Specifically, we dig
deeper into the properties of BSN to make it more suitable for real noise.
Surprisingly, we find that adding an appropriate perturbation to the training
images can effectively improve the performance of BSN. Further, we propose that
the sampling difference can be considered as perturbation to achieve better
results. Finally we propose a new BSN framework in combination with our RSG
strategy. The results show that it significantly outperforms other
state-of-the-art self-supervised denoising methods on real-world datasets. The
code is available at https://github.com/p1y2z3/SDAP.