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

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.

Automated summary

This paper reviews the development of image super-resolution technology, focusing on convolutional neural network-based algorithms. Different algorithms were compared in terms of datasets used, loss functions, evaluation metrics, etc., with the advantages and disadvantages of each upsampling module and design technique summarized.