Single Image Super-Resolution Using Multi-Scale Deep Encoder-Decoder with Phase Congruency Edge Map Guidance

Liu, Heng, Fu, Zilin, Han, Jungong, Shao, Ling, Hou, Shudong and Chu, Yuezhong (2019) Single Image Super-Resolution Using Multi-Scale Deep Encoder-Decoder with Phase Congruency Edge Map Guidance. Information Sciences, 473. pp. 44-58. ISSN 0020-0255

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    Abstract

    This paper presents an end-to-end multi-scale deep encoder (convolution) and decoder (deconvolution) network for single image super-resolution (SISR) guided by phase congruency (PC) edge map. Our system starts by a single scale symmetrical encoder-decoder structure for SISR, which is extended to a multi-scale model by integrating wavelet multi-resolution analysis into our network. The new multi-scale deep learning system allows the low resolution (LR) input and its PC edge map to be combined so as to precisely predict the multi-scale super-resolved edge details with the guidance of the high-resolution (HR) PC edge map. In this way, the proposed deep model takes both the reconstruction of image pixels’ intensities and the recovery of multi-scale edge details into consideration under the same framework. We evaluate the proposed model on benchmark datasets of different data scenarios, such as Set14 and BSD100 - natural images, Middlebury and New Tsukuba - depth images. The evaluations based on both PSNR and visual perception reveal that the proposed model is superior to the state-of-the-art methods.

    Item Type: Article
    Uncontrolled Keywords: single image super-resolution,multi-scale deep mode,ldeep encoder-decoder,phase congruency edge map
    Faculty \ School: Faculty of Science > School of Computing Sciences
    Related URLs:
    Depositing User: LivePure Connector
    Date Deposited: 19 Sep 2018 09:30
    Last Modified: 09 Apr 2019 13:45
    URI: https://ueaeprints.uea.ac.uk/id/eprint/68316
    DOI: 10.1016/j.ins.2018.09.018

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