Review of Super-Resolution Image Reconstruction Algorithms

Abstract

In human visual perception system, high-resolution (HR) image is an important medium to clearly express its spatial structure, detailed features, edge texture and other information, and it has a very wide range of practical value in medicine, criminal investigation, satellite and other fields. Super-resolution image reconstruction (SRIR) is a key research task in the field of computer vision and image processing, which aims to reconstruct a high-resolution image with clear details from a given low-resolution (LR) image. In this paper, the concept and mathematical model of super-resolution image reconstruction are firstly described, and the image reconstruction methods are systematically classified into three kinds of super-resolution image reconstruction methods：based on interpolation, based on reconstruction, based on learning (before and after deep learning). Secondly, the typical, commonly used and latest algorithms among the three methods and their research are comprehensively reviewed and summarized, and the listed image reconstruction algorithms are combed from the aspects of network structure, learning mechanism, application scenarios, advantages and limitations. Then, the datasets and image quality evaluation indices used for super-resolution image reconstruction algorithms are summarized, and the characteristics and performance of various super-resolution image reconstruction algorithms based on deep learning are compared. Finally, the future research direction or angle of super-resolution image reconstruction is prospected from four aspects.

Keywords

• |image processing|super-resolution reconstruction|deep learning|image quality assessment