Abstract
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.
Highlights
Image denoising is one of the most fundamental problems in computer vision and image processing fields
We analyzed the effect of channel attention on image denoising, and showed that channel attention has an adaptive nature to image contents and noise
We proposed a locally adaptive channel attention module and an image denoising network, LACANDI, based on it
Summary
Image denoising is one of the most fundamental problems in computer vision and image processing fields. The result using the attention weights of σ = 25 (Fig. 8(c)) has clearly restored details and no remaining noise This behavior of channel attention is analogous to denoising strength parameters of traditional denoising algorithms such as the range sigma of the bilateral filter [10], and shows that channel attention adapts the network to more effectively remove noise with respect to different noise levels. B. GRID SIZE If we set kw = W and kh = H for an input image of size W × H and use a mean filter of size W × H , we can compute locally adaptive channel attention weights for all pixels.
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