Deep learning based single image super-resolution: a survey

Viet Khanh Ha, Jinchang Ren, Xinying Xu, Sophia Zhao, Gang Xie, Valentin Masero Vargas

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

27 Citations (Scopus)
11 Downloads (Pure)


Image super-resolution is a process of obtaining one or more high-resolution image from single or multiple samples of low-resolution images. Due to its wide applications, a number of different techniques have been developed recently, including interpolation-based, reconstruction-based and learning-based. The learning-based methods have recently attracted increasing great attention due to their capability in predicting the high-frequency details lost in low resolution image. This survey mainly provides an overview on most of published work for single image reconstruction using Convolutional Neural Network. Furthermore, common issues in super-resolution algorithms, such as imaging models, improvement factor and assessment criteria are also discussed.

Original languageEnglish
Title of host publicationInternational Conference on Brain Inspired Cognitive Systems - BICS 2018
Subtitle of host publicationAdvances in Brain Inspired Cognitive Systems
EditorsAmir Hussain, Bin Luo, Jiangbin Zheng, Xinbo Zhao, Cheng-Lin Liu, Jinchang Ren, Huimin Zhao
Place of PublicationCham, Switzerland
Number of pages14
ISBN (Electronic)978-3-030-00563-4
ISBN (Print)9783030005627
Publication statusPublished - 6 Oct 2018
Event9th International Conference on Brain-Inspired Cognitive Systems, BICS 2018 - Xi'an, China
Duration: 7 Jul 20188 Jul 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10989 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference9th International Conference on Brain-Inspired Cognitive Systems, BICS 2018


  • convolutional neural network
  • high-resolution image
  • image super resolution


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