Research Area:  Machine Learning

Abstract:

Image super-resolution is a process of obtaining one or more high-resolution image from single or multiple samples of low-resolution images. Due to its wide applications, a number of different techniques have been developed recently, including interpolation-based, reconstruction-based and learning-based. The learning-based methods have recently attracted increasing great attention due to their capability in predicting the high-frequency details lost in low resolution image. This survey mainly provides an overview on most of published work for single image reconstruction using Convolutional Neural Network. Furthermore, common issues in super-resolution algorithms, such as imaging models, improvement factor and assessment criteria are also discussed.

Keywords:  
Deep Learning
Single Image Super-Resolution
Machine Learning

Author(s) Name:  Viet Khanh Ha, Jinchang Ren, Xinying Xu, Sophia Zhao, Gang Xie & Valentin Masero Vargas

Publisher name:  Springer

DOI:  10.1007/978-3-030-00563-4_11

Volume Information:  

Paper Link:   https://link.springer.com/chapter/10.1007/978-3-030-00563-4_11