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PNEN: Pyramid Non-Local Enhanced Networks.

Authors :
Zhu, Feida
Fang, Chaowei
Ma, Kai-Kuang
Source :
IEEE Transactions on Image Processing. 2020, Vol. 29, p8831-8841. 11p.
Publication Year :
2020

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10577149
Volume :
29
Database :
Academic Search Index
Journal :
IEEE Transactions on Image Processing
Publication Type :
Academic Journal
Accession number :
170078599
Full Text :
https://doi.org/10.1109/TIP.2020.3019644