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.

中文翻译：

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基于学习的单图像超分辨率重建技术综述

摘要

随着信息技术的发展，对高分辨率图像的需求很高。图像超分辨率重建技术是在信号处理技术的帮助下，从一个或一系列低分辨率图像中估算出质量更高的高分辨率图像。核心思想是根据需要集成有用的信息以及来自单个图像或多个图像的强相关性和互补性。基于学习的单图像超分辨率重建技术是当前的研究热点。本文系统地概述了该技术，并讨论了该技术的一些主要类别，例如基于邻居的超分辨率重建，基于稀疏表示的深度学习。在本文的最后，