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Research on image super-resolution based on attention mechanism and multi-scale

Journal of physics. Conference series, 2021-02, Vol.1792 (1), p.12025 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;2021. 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: 10.1088/1742-6596/1792/1/012025

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  • Title:
    Research on image super-resolution based on attention mechanism and multi-scale
  • Author: Ren, Xiaokang ; Li, Xingzhen
  • Subjects: Algorithms ; Image resolution ; Image restoration
  • Is Part Of: Journal of physics. Conference series, 2021-02, Vol.1792 (1), p.12025
  • Description: In order to solve the problem of the single feature scale of the generated image in the SISR field and the lack of texture information, a parallel generation confrontation network structure based on the attention mechanism and multi-scale is proposed on the basis of SRGAN, which adopts a dual generator and discriminator combined with attention module model. Train the network to learn multi-scale features, and integrate high-frequency information of different scales in the residual network. The experimental results on Set5, Set14, and BSD100 benchmark data sets prove that the algorithm has a good effect in restoring image detail information.
  • Publisher: Bristol: IOP Publishing
  • Language: English
  • Identifier: ISSN: 1742-6588
    EISSN: 1742-6596
    DOI: 10.1088/1742-6596/1792/1/012025
  • 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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