Deep learning based single image super-resolution : a survey

Ha, Viet Khanh and Ren, Jinchang and Xu, Xinying and Zhao, Sophia and Xie, Gang and Vargas, Valentin Masero; Hussain, Amir and Luo, Bin and Zheng, Jiangbin and Zhao, Xinbo and Liu, Cheng-Lin and Ren, Jinchang and Zhao, Huimin, eds. (2018) Deep learning based single image super-resolution : a survey. In: International Conference on Brain Inspired Cognitive Systems - BICS 2018. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) . Springer-Verlag, CHN, pp. 106-119. ISBN 978-3-030-00563-4

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    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.