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Open AccessReview

Deep Learning Algorithms for Single Image Super-Resolution: A Systematic Review

School of Electrical & Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal 14300, Pulau Pinang, Malaysia
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Academic Editors: Nikolaos Mitianoudis and Georgios Tzimiropoulos
Electronics 2021, 10(7), 867; https://doi.org/10.3390/electronics10070867
Received: 7 March 2021 / Revised: 31 March 2021 / Accepted: 31 March 2021 / Published: 6 April 2021
(This article belongs to the Special Issue Deep Learning Technologies for Machine Vision and Audition)
Image super-resolution has become an important technology recently, especially in the medical and industrial fields. As such, much effort has been given to develop image super-resolution algorithms. A recent method used was convolutional neural network (CNN) based algorithms. super-resolution convolutional neural network (SRCNN) was the pioneer of CNN-based algorithms, and it continued being improved till today through different techniques. The techniques included the type of loss functions used, upsampling module deployed, and the adopted network design strategies. In this paper, a total of 18 articles were selected through the PRISMA standard. A total of 19 algorithms were found in the selected articles and were reviewed. A few aspects are reviewed and compared, including datasets used, loss functions used, evaluation metrics applied, upsampling module deployed, and adopted design techniques. For each upsampling module and design techniques, their respective advantages and disadvantages were also summarized. View Full-Text
Keywords: deep learning; single image; super-resolution; systematic review deep learning; single image; super-resolution; systematic review
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MDPI and ACS Style

Ooi, Y.K.; Ibrahim, H. Deep Learning Algorithms for Single Image Super-Resolution: A Systematic Review. Electronics 2021, 10, 867. https://doi.org/10.3390/electronics10070867

AMA Style

Ooi YK, Ibrahim H. Deep Learning Algorithms for Single Image Super-Resolution: A Systematic Review. Electronics. 2021; 10(7):867. https://doi.org/10.3390/electronics10070867

Chicago/Turabian Style

Ooi, Yoong K.; Ibrahim, Haidi. 2021. "Deep Learning Algorithms for Single Image Super-Resolution: A Systematic Review" Electronics 10, no. 7: 867. https://doi.org/10.3390/electronics10070867

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