International Journal of Soft Computing
ISSN: OnlineISSN: Print 1816-9503
Abstract
The super‐resolution of images has seen a remarkable progress especially with the use of deep learning models. This technique allows to have a better‐quality image from one or more low resolution versions. Super‐resolution therefore aims at enriching a low‐resolution image with additional pixel density and high frequency detail. This study presents a comprehensive empirical study based on a systematic review of deep learning‐based models for Single Image Super‐Resolution (SISR), exploring the set of techniques offered by deep learning technology and used for SISR. In this paper, we present a global and complete state of the art on machine learning models based on reference metrics (mainly Peak Signal to Noise Ratio‐PSNR‐ and Structural SIMilarity‐SSIM‐) in the field of computer visualization and image reconstruction. This study was done on several machine learning designs with 90 different models tested on 7 reference datasets in the computer vision domain. Thus, our goal is to present a benchmark to demonstrate the performance and limitations of these models as well as to guide future research in the field of super image resolution in order to develop efficient algorithms. Moreover, our study covers different neural network architectures (Generative Adversarial Networks ‐GAN‐, Convolutional Neural Network ‐CNN‐, Recurrent Neural Network ‐RNN‐...), using different techniques and technologies.
How to cite this article:
Omar Soufi, Zineb Aarab, omar soufi and Fatima Zahra Belouadha. Study and Analyze of Deep Learning Based Models for Single Image Super-Resolution.
DOI: https://doi.org/10.36478/10.59218\makijsc.2022.13.26
URL: https://www.makhillpublications.co/view-article/1816-9503/10.59218\makijsc.2022.13.26