  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.
