Spatial-Adaptive Network for Single Image Denoising
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.
PDF Abstract ECCV 2020 PDF ECCV 2020 Abstract
Tasks
BSD
SIDD
DND
RENOIR
Reproducibility Reports
We have achieved to reproduce the results qualitatively and quantitatively on synthetic and noise removal tasks. SADNet has the capacity to learn to remove the synthetic and real noise in images, and it produces visually-plausible outputs even after a few epochs. Moreover, we have employed SSIM and PSNR metrics to measure the quantitative performance for all settings. The quantitative results on both tasks are on-par when compared to the reported results in the paper.
Results from the Paper
Ranked #7 on
Image Denoising
on DND
(using extra training data)
