Single image super-resolution using multi-scale deep encoder-decoder with phase congruency edge map guidance

Heng Liu, Zilin Fu, Jungong Han, Ling Shao, Shudong Hou, Yuezhong Chu

Research output: Contribution to journalArticlepeer-review

56 Citations (Scopus)
6 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)44-58
Number of pages15
JournalInformation Sciences
Early online date18 Sep 2018
Publication statusPublished - Jan 2019


  • Single image super-resolution
  • Multi-scale deep mode
  • lDeep encoder-decoder
  • Phase congruency edge map

Cite this