  Non-local self-similarity in natural images has been verified to be an
effective prior for image restoration. However, most existing deep non-local
methods assign a fixed number of neighbors for each query item, neglecting the
dynamics of non-local correlations. Moreover, the non-local correlations are
usually based on pixels, prone to be biased due to image degradation. To
rectify these weaknesses, in this paper, we propose a dynamic attentive graph
learning model (DAGL) to explore the dynamic non-local property on patch level
for image restoration. Specifically, we propose an improved graph model to
perform patch-wise graph convolution with a dynamic and adaptive number of
neighbors for each node. In this way, image content can adaptively balance
over-smooth and over-sharp artifacts through the number of its connected
neighbors, and the patch-wise non-local correlations can enhance the message
passing process. Experimental results on various image restoration tasks:
synthetic image denoising, real image denoising, image demosaicing, and
compression artifact reduction show that our DAGL can produce state-of-the-art
results with superior accuracy and visual quality. The source code is available
at https://github.com/jianzhangcs/DAGL.

Computer Vision and Pattern Recognition