Deep Residual Neural Network Design for Super-Resolution Imaging

Wei Ting Chen, Pei Yin Chen, Bo Chen Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Convolution neural network recently confirmed the high-quality reconstruction for single-image super-resolution (SR). In this paper we present a Deep Level Residual Network (DLNR), a low-memory effective neural network to reconstruct super-resolution images. This neural network also has the following characteristics. (1) Ability to perform different convolution size operations on the image which can achieve more comprehensive feature extraction effects. (2) Using residual learning to expand the depth of the network and increase the capacity of learning. (3) Taking the skill of parameter sharing between the network module to reduce the number of parameters. After the experiment, we find that DLNR can achieve 37.78 in PSNR and 0.975 in SSIM when using Manga109 as testing set for 2× SR.

Original languageEnglish
Title of host publicationNew Trends in Computer Technologies and Applications - 23rd International Computer Symposium, ICS 2018, Revised Selected Papers
EditorsChuan-Yu Chang, Chien-Chou Lin, Horng-Horng Lin
PublisherSpringer Verlag
Number of pages11
ISBN (Print)9789811391897
Publication statusPublished - 2019
Event23rd International Computer Symposium, ICS 2018 - Yunlin, Taiwan
Duration: 2018 Dec 202018 Dec 22

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference23rd International Computer Symposium, ICS 2018

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Mathematics(all)


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