Deep learning based single image super-resolution: a survey

Viet Khanh Ha, Jin Chang Ren*, Xin Ying Xu, Sophia Zhao, Gang Xie, Valentin Masero, Amir Hussain

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

65 Citations (Scopus)
41 Downloads (Pure)


Single image super-resolution has attracted increasing attention and has a wide range of applications in satellite imaging, medical imaging, computer vision, security surveillance imaging, remote sensing, objection detection, and recognition. Recently, deep learning techniques have emerged and blossomed, producing “the state-of-the-art” in many domains. Due to their capability in feature extraction and mapping, it is very helpful to predict high-frequency details lost in low-resolution images. In this paper, we give an overview of recent advances in deep learning-based models and methods that have been applied to single image super-resolution tasks. We also summarize, compare and discuss various models from the past and present for comprehensive understanding and finally provide open problems and possible directions for future research.

Original languageEnglish
Pages (from-to)413-426
Number of pages14
JournalInternational Journal of Automation and Computing
Issue number4
Early online date19 Jul 2019
Publication statusPublished - 31 Aug 2019


  • convolutional neural network
  • deep learning
  • high-resolution image
  • image super-resolution
  • low-resolution image


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