PNEN: Pyramid Non-Local Enhanced Networks

• URL: http://arxiv.org/abs/2008.09742v1
• Date: Sat, 22 Aug 2020 03:10:48 GMT
• Title: PNEN: Pyramid Non-Local Enhanced Networks
• Authors: Feida Zhu, Chaowei Fang, Kai-Kuang Ma
• Abstract summary: We propose a novel non-local module, Pyramid Non-local Block, to build up connection between every pixel and all remain pixels. Based on the proposed module, we devise a Pyramid Non-local Enhanced Networks for edge-preserving image smoothing. We integrate it into two existing methods for image denoising and single image super-resolution, achieving consistently improved performance.
• Score: 23.17149002568982
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing neural networks proposed for low-level image processing tasks are usually implemented by stacking convolution layers with limited kernel size. Every convolution layer merely involves in context information from a small local neighborhood. More contextual features can be explored as more convolution layers are adopted. However it is difficult and costly to take full advantage of long-range dependencies. We propose a novel non-local module, Pyramid Non-local Block, to build up connection between every pixel and all remain pixels. The proposed module is capable of efficiently exploiting pairwise dependencies between different scales of low-level structures. The target is fulfilled through first learning a query feature map with full resolution and a pyramid of reference feature maps with downscaled resolutions. Then correlations with multi-scale reference features are exploited for enhancing pixel-level feature representation. The calculation procedure is economical considering memory consumption and computational cost. Based on the proposed module, we devise a Pyramid Non-local Enhanced Networks for edge-preserving image smoothing which achieves state-of-the-art performance in imitating three classical image smoothing algorithms. Additionally, the pyramid non-local block can be directly incorporated into convolution neural networks for other image restoration tasks. We integrate it into two existing methods for image denoising and single image super-resolution, achieving consistently improved performance.

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