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Dual-Branch Feature Fusion Network for Single Image Super-Resolution

Journal of physics. Conference series, 2020-11, Vol.1651 (1), p.12167 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;2020. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1742-6588 ;EISSN: 1742-6596 ;DOI: 10.1088/1742-6596/1651/1/012167

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  • Title:
    Dual-Branch Feature Fusion Network for Single Image Super-Resolution
  • Author: Wang, Kai ; Duanmu, Chunjiang
  • Subjects: Convolution ; Feature extraction ; Image resolution
  • Is Part Of: Journal of physics. Conference series, 2020-11, Vol.1651 (1), p.12167
  • Description: Rcently, the research of single image super-resolution (SISR) based on deep learning has made great progress. However, most of the methods of super-resolution (SR) study use a simple chain structure to obtain higher super-resolution performance. Additionally, most methods do not fully utilize the hierarchical features generated in the middle of the network, thereby cannot achieve relatively high performance. In this paper, we propose a novel dual-branch feature fusion network (DBFFN) to address this two problems in image SR. The backbone of DBFFN is composed of multiple dual-branch feature fusion block (DBFFB) cascaded. The DBFFB has a dual-branch structure which mainly contains two parallel sub-networks, one extracts image fine features via densely connected convolution layers, the other one sub-network extracts image more contextual features by stacking dilated convolution layers. The features extracted from each hierarchical of the DBFFB are cascaded, and then feature fusion is performed. A skip connection is introduced to learn residual between input and output of the DBFFB. Especially, we use the deconvolution layer at the end of the network to enlarge the image. The results of the experiment are exciting, when the scale factor is 3, the performance of our network has 0.56dB, 0.6dB, and 0.79dB improvement on the set5, set14, and urban100 benchmark datasets compared with RDN.
  • Publisher: Bristol: IOP Publishing
  • Language: English
  • Identifier: ISSN: 1742-6588
    EISSN: 1742-6596
    DOI: 10.1088/1742-6596/1651/1/012167
  • Source: Open Access: IOP Publishing Free Content
    Geneva Foundation Free Medical Journals at publisher websites
    AUTh Library subscriptions: ProQuest Central
    IOPscience (Open Access)
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