Single-Image Super-Resolution: A Survey

  • Tingting Yao
  • Yu Luo
  • Yantong Chen
  • Dongqiao Yang
  • Lei ZhaoEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)


Single-image super-resolution has been broadly applied in many fields such as military term, medical imaging, etc. In this paper, we mainly focus on the researches of recent years and classify them into non-deep learning SR algorithms and deep learning SR algorithms. For each classification, the basic concepts and algorithm processes are introduced. Furthermore, the paper discusses the advantages and disadvantages of different algorithms, which will offer potential research direction for the future development of SR.


Single-image super-resolution Learning-based Deep learning Survey 



This work was supported by the National Natural Science Foundation of China (No. 31700742), the Young Elite Scientist Sponsorship Program by CAST (2017QNRC001) and the Fundamental Research Funds for the Central Universities (No. 3132018306, 3132018180, 3132018172).


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Tingting Yao
    • 1
  • Yu Luo
    • 1
  • Yantong Chen
    • 1
  • Dongqiao Yang
    • 1
  • Lei Zhao
    • 2
    Email author
  1. 1.College of Information Science and Technology, Collaborative Innovation Research Institute of Autonomous ShipDalian Maritime UniversityDalianChina
  2. 2.Institute of Environmental Systems Biology, College of Environmental Science and EngineeringDalian Maritime UniversityDalianChina

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