  Local and non-local attention-based methods have been well studied in various
image restoration tasks while leading to promising performance. However, most
of the existing methods solely focus on one type of attention mechanism (local
or non-local). Furthermore, by exploiting the self-similarity of natural
images, existing pixel-wise non-local attention operations tend to give rise to
deviations in the process of characterizing long-range dependence due to image
degeneration. To overcome these problems, in this paper we propose a novel
collaborative attention network (COLA-Net) for image restoration, as the first
attempt to combine local and non-local attention mechanisms to restore image
content in the areas with complex textures and with highly repetitive details
respectively. In addition, an effective and robust patch-wise non-local
attention model is developed to capture long-range feature correspondences
through 3D patches. Extensive experiments on synthetic image denoising, real
image denoising and compression artifact reduction tasks demonstrate that our
proposed COLA-Net is able to achieve state-of-the-art performance in both peak
signal-to-noise ratio and visual perception, while maintaining an attractive
computational complexity. The source code is available on
https://github.com/MC-E/COLA-Net.
