Deep learning methods in real-time image super-resolution: a survey
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Deep learning methods in real‑time image super‑resolution: a survey Xiaofang Li2 · Yirui Wu1 · Wen Zhang1 · Ruichao Wang3 · Feng Hou4 Received: 30 April 2019 / Accepted: 30 October 2019 © Springer-Verlag GmbH Germany, part of Springer Nature 2019
Abstract Super-resolution is generally defined as a process to obtain high-resolution images form inputs of low-resolution observations, which has attracted quantity of attention from researchers of image-processing community. In this paper, we aim to analyze, compare, and contrast technical problems, methods, and the performance of super-resolution research, especially real-time super-resolution methods based on deep learning structures. Specifically, we first summarize fundamental problems, perform algorithm categorization, and analyze possible application scenarios that should be considered. Since increasing attention has been drawn in utilizing convolutional neural networks (CNN) or generative adversarial networks (GAN) to predict high-frequency details lost in low- resolution images, we provide a general overview on background technologies and pay special attention to super-resolution methods built on deep learning architectures for real-time super-resolution, which not only produce desirable reconstruction results, but also enlarge possible application scenarios of super resolution to systems like cell phones, drones, and embedding systems. Afterwards, benchmark datasets with descriptions are enumerated, and performance of most representative super-resolution approaches is provided to offer a fair and comparative view on performance of current approaches. Finally, we conclude the paper and suggest ways to improve usage of deep learning methods on real-time image super-resolution. Keywords Image super-resolution · Real-time processing · Deep learning · Convolutional neural network · Generative adversarial network
1 Introduction
* Yirui Wu [email protected] Xiaofang Li [email protected] Wen Zhang [email protected] Ruichao Wang [email protected] Feng Hou [email protected] 1
College of Computer and Information, Hohai University, Nanjing, China
2
School of Computer Science and Information Engineering, Changzhou Institute of Technology, Changzhou, China
3
School of Computer Science, University College Dublin, Dublin, Ireland
4
School of Natural and Computational Science, Massey University, Palmerston North, New Zealand
In image processing area, image generally describes more visual details with higher resolution. To better understand semantic meanings of real-world images, it is an essential task for researchers to provide high-resolution (HR) images with sharp and clear object boundary or rich visual descriptions. However, obtaining HR images by possible hardware-based approaches is difficult and expensive [74]. For example, one of the possible methods, i.e., decreasing the pixel size would decrease the amount of light achieved by sensors, results in shot noise and sensitivity to diffraction effects. Another possible way, i.e., increasin
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