Viet Khanh Ha, Jin-Chang Ren, Xin-Ying Xu, Sophia Zhao, Gang Xie, Valentin Masero and Amir Hussain. Deep Learning Based Single Image Super-resolution: A Survey. International Journal of Automation and Computing, vol. 16, no. 4, pp. 413-426, 2019. DOI: 10.1007/s11633-019-1183-x
Citation: Viet Khanh Ha, Jin-Chang Ren, Xin-Ying Xu, Sophia Zhao, Gang Xie, Valentin Masero and Amir Hussain. Deep Learning Based Single Image Super-resolution: A Survey. International Journal of Automation and Computing, vol. 16, no. 4, pp. 413-426, 2019. DOI: 10.1007/s11633-019-1183-x

Deep Learning Based Single Image Super-resolution: A Survey

  • 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.
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