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Existing neural systems suggested for low-level image processing jobs are usually implemented by stacking convolution layers with minimal kernel size. Every convolution level just requires in framework information from a small regional neighbor hood. Much more contextual functions is explored as more convolution layers are adopted. Nevertheless it is difficult and expensive to make the most of long-range dependencies. We suggest a novel non-local component, Pyramid Non-local Block, to produce connection
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