Survey of Learning Based Single Image Super-Resolution Reconstruction Technology
Kejia Bai, Xiaofeng Liao, Q. Zhang, Xu Jia, Shaopeng Liu
Abstract
With the development of information technology, there is a high demand for high-resolution images. Image super-resolution reconstruction technology is to estimate a high-resolution image with better quality from one or a sequence of low-resolution images, with the help of signal processing technology. The core idea is to integrate useful information with strong correlations and complementarities from single image or multiple images as desired. Learning based single image super-resolution reconstruction technology is the current research hotspot. The paper systematically overviews this technology and discuss some main categories of it, such as super-resolution reconstruction based on neighbors, based on sparse representation, based on deep learning. At the end of the paper, challenge issues and future research directions for super-resolution image reconstruction are put forward.