Previous works have shown that convolutional neural networks can achieve good
performance in image denoising tasks. However, limited by the local rigid
convolutional operation, these methods lead to oversmoothing artifacts. A
deeper network structure could alleviate these problems, but more computational
overhead is needed. In this paper, we propose a novel spatial-adaptive
denoising network (SADNet) for efficient single image blind noise removal. To
adapt to changes in spatial textures and edges, we design a residual
spatial-adaptive block. Deformable convolution is introduced to sample the
spatially correlated features for weighting. An encoder-decoder structure with
a context block is introduced to capture multiscale information. With noise
removal from the coarse to fine, a high-quality noisefree image can be
obtained. We apply our method to both synthetic and real noisy image datasets.
The experimental results demonstrate that our method can surpass the
state-of-the-art denoising methods both quantitatively and visually.

本文提出了一种新颖的空间自适应去噪网络（SADNet），使用卷积神经网络，设计了残差空间自适应块，在采样时引入可变变形卷积以加权样本的空间相关特征，并引入了具有上下文块的编码器 - 解码器结构以捕获多尺度信息，通过从粗到细的噪声去除，可以获得高质量的无噪声图像。在实验结果中，我们的方法在量化和视觉上均优于现有的去噪方法。