The key objective of super-resolution imaging is the process of reconstructing a high resolution image from one or a set of low resolution images, to overcome the ill-posed conditions of the image capturing process to get better visualization. It has found practical applications in many real world problems such as surveillance, medical imaging, text image analysis, biometrics, satellite imaging, to name a few. This has encouraged many researchers to develop a new super resolution algorithm for a particular purpose. The objective of the review paper is to explore the different image super resolution algorithms used to enhance the low resolution images to high resolution images. This survey on super resolution algorithms will help the researchers to comprehend the effectiveness of the super resolution process and will make easy to develop advanced super resolution methods.

Anand Deshpande1, Prashant P Patavardhan2
Angadi Institute of Technology and Management, India1, Gogte Institute of Technology, India2

Super Resolution, PSNR, Frequency Domain, Gaussian Process Regression, Total Variation
Published By :
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 9 , Issue: 3 )
Date of Publication :
February 2019

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.