DA-BSN: Self-Supervised Real-World Image Denoising based on Dynamic Adaptive Blind Spot Network
Hezhen Xia, Hongyi Liu, Zhihui Wei · 2024
Blind spot network (BSN) is an effective method for self-supervised image denoising, but real-word noises violate its basic assumption of pixel-wise independent noise. Furthermore, the existing CNN-based BSNs use a uniform convolutional kernel to predict the masked pixels across different patches, which limit the denoising performance. Based on this, we propose a novel blind spot network called dynamic adaptive blind spot network (DA-BSN) that can be self-adjustable according to different spatial geometric structures of the image. Specifically, we design a dynamic adaptive blind convolution (DAB-Conv) block that can accurately predict clean pixels without introducing noise from irrelevant pixels by learning the relationship between masked pixels and surrounding pixels of different image structure. The results on public real-world datasets demonstrate that our method significantly outperforms existing self-supervised denoising methods and achieves great efficiency.