Synergic Feature Attention for Image Restoration

Local and non-local attentions are both effective methods in the domain of image restoration (IR). However, most existing image restoration methods use these two strategies indiscriminately, and how to make a trade-off between local and non-local attention operations has hardly been studied. Furthermore, the commonly used pixel-wise non-local operation tends to be biased during image restoration due to the image degeneration. To overcome these problems, in this paper, we propose a novel Synergic Attention Network (SAT-Net) for image restoration as an inventive attempt to combine local and non-local attention mechanisms to restore complex textures and highly repetitive details distinguishingly. We also propose an effective patch-wise non-local attention method to establish more reliable long-range dependences based on 3D patches. Experimental results on synthetic image denoising, real image denoising, and compression artifact reduction tasks show that our proposed model can achieve state-of-the-art performance under objective and subjective evaluations.