关键词: 深度学习, 图像超分辨率, 有监督学习, 无监督学习

Abstract:

Image super-resolution (SR) is an important type of image processing technology for improving image and video resolution in computer vision. In recent years, thanks to the success of neural networks, image super-resolution technology based on deep learning is booming, which is undoubtedly the mainstream direction of super-resolution technology research. In this paper, the research progress of image super-resolution is summarized. First of all, the existing super-resolution techniques are summarized and introduced according to their input and output. What??s more, the single-image super-resolution technique based on deep learning is discussed from two aspects of supervised and unsupervised learning respectively, and some representative latest super-resolution reconstruction technologies are introduced. Then, the related problems of super-resolution technology, namely performance evalua-tion indicators and standard data sets, are discussed, and several typical algorithms are compared by experiments. Finally, the future research trend of image super-resolution algorithm is prospected.

Key words: deep learning, image super-resolution, supervised learning, unsupervised learning