Significant progress has been made in self-supervised image denoising (SSID)
in the recent few years. However, most methods focus on dealing with spatially
independent noise, and they have little practicality on real-world sRGB images
with spatially correlated noise. Although pixel-shuffle downsampling has been
suggested for breaking the noise correlation, it breaks the original
information of images, which limits the denoising performance. In this paper,
we propose a novel perspective to solve this problem, i.e., seeking for
spatially adaptive supervision for real-world sRGB image denoising.
Specifically, we take into account the respective characteristics of flat and
textured regions in noisy images, and construct supervisions for them
separately. For flat areas, the supervision can be safely derived from
non-adjacent pixels, which are much far from the current pixel for excluding
the influence of the noise-correlated ones. And we extend the blind-spot
network to a blind-neighborhood network (BNN) for providing supervision on flat
areas. For textured regions, the supervision has to be closely related to the
content of adjacent pixels. And we present a locally aware network (LAN) to
meet the requirement, while LAN itself is selectively supervised with the
output of BNN. Combining these two supervisions, a denoising network (e.g.,
U-Net) can be well-trained. Extensive experiments show that our method performs
favorably against state-of-the-art SSID methods on real-world sRGB photographs.
The code is available at this https URL

本研究提出一种新的思路来解决现实世界 sRGB 图像去噪中空间相关噪声的问题，其通过分别考虑噪声图像中平滑区域和纹理区域的特性，并构建相应的监督来训练模型，最终得到了更好的去噪效果。