Locally Adaptive Channel Attention-Based Network for Denoising Images

Channel attention has recently been proposed and shown a great improvement in image classification accuracy. In this paper, we show that channel attention can greatly help a low-level vision task, image denoising, as well, and propose channel attention-based networks for image denoising. We provide a thorough analysis on the effect of channel attention on image denoising, which shows that channel attention boosts denoising performance by making the network to focus on informative channels more closely related to noise. We also show that channel attention has an adaptive nature to image contents and noise and propose locally adaptive channel attention for further improving image denoising quality. Experimental results show that our denoising network with global channel attention outperforms existing state-of-the-art methods in both blind and non-blind settings, and our locally adaptive channel attention substantially improves both image quality and computation time.