A Review of Deep Learning Based Image Super-resolution Techniques

TL;DR

This paper reviews recent advancements in deep learning techniques for image super-resolution, summarizing research progress, applications, and future prospects in enhancing image resolution using neural networks.

Contribution

It provides a comprehensive overview of deep learning-based image super-resolution methods, highlighting recent progress and potential future directions.

Findings

Deep learning methods have significantly improved image super-resolution quality.

The paper summarizes recent research and application results in the field.

It discusses future prospects for deep learning in super-resolution technology.

Abstract

Image super-resolution technology is the process of obtaining high-resolution images from one or more low-resolution images. With the development of deep learning, image super-resolution technology based on deep learning method is emerging. This paper reviews the research progress of the application of depth learning method in the field of image super-resolution, introduces this kind of super-resolution work from several aspects, and looks forward to the further application of depth learning method in the field of image super-resolution. By collecting and counting the relevant literature on the application of depth learning in the field of image super-resolution, we preliminarily summarizes the application results of depth learning method in the field of image super-resolution, and reports the latest progress of image super-resolution technology based on depth learning method.