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

[img]
Preview
PDF (Accepted manuscript) - Submitted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (10MB) | Preview

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 08:30
Last Modified: 27 May 2020 23:58
URI: https://ueaeprints.uea.ac.uk/id/eprint/68316
DOI: 10.1016/j.ins.2018.09.018

Actions (login required)

View Item View Item