In recent years, the development of deep learning has been pushing image
denoising to a new level. Among them, self-supervised denoising is increasingly
popular because it does not require any prior knowledge. Most of the existing
self-supervised methods are based on convolutional neural networks (CNN), which
are restricted by the locality of the receptive field and would cause color
shifts or textures loss. In this paper, we propose a novel Denoise Transformer
for real-world image denoising, which is mainly constructed with Context-aware
Denoise Transformer (CADT) units and Secondary Noise Extractor (SNE) block.
CADT is designed as a dual-branch structure, where the global branch uses a
window-based Transformer encoder to extract the global information, while the
local branch focuses on the extraction of local features with small receptive
field. By incorporating CADT as basic components, we build a hierarchical
network to directly learn the noise distribution information through residual
learning and obtain the first stage denoised output. Then, we design SNE in low
computation for secondary global noise extraction. Finally the blind spots are
collected from the Denoise Transformer output and reconstructed, forming the
final denoised image. Extensive experiments on the real-world SIDD benchmark
achieve 50.62/0.990 for PSNR/SSIM, which is competitive with the current
state-of-the-art method and only 0.17/0.001 lower. Visual comparisons on public
sRGB, Raw-RGB and greyscale datasets prove that our proposed Denoise
Transformer has a competitive performance, especially on blurred textures and
low-light images, without using additional knowledge, e.g., noise level or
noise type, regarding the underlying unknown noise.

本文提出了一种新颖的 Denoise Transformer 算法，其中包括 CADT 单元和 SNE 块，通过二者的结合构建分层网络，直接利用残差学习获取噪声分布信息，进而形成最终的去噪输出。实验证明，该算法在传统深度学习方法中具有更好的性能和岩石文本以及低光图像，而不需要关于噪声级别或类型的任何先前知识。