A Review of Image Super Resolution using Deep Learning

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Published: May 17, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i5s.6638
Keywords:
Image Super Resolution, medical imaging, deep learning, MRI, CT

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Sneha R. Mhatre
PhD Research Scholar, Pillai HOC College of Engg. and Technology, Comp. Engg., University of Mumbai, Maharashtra, India
Jagdish W. Bakal
Professor and Principal, Pillai HOC College of Engg. and Technology, University of Mumbai, Maharashtra, India

Abstract

The image processing methods collectively known as super-resolution have proven useful in creating high-quality images from a group of low-resolution photographic images. Single image super resolution (SISR) has been applied in a variety of fields. The paper offers an in-depth analysis of a few current picture super resolution works created in various domains. In order to comprehend the most current developments in the development of Image super resolution systems, these recent publications have been examined with particular emphasis paid to the domain for which these systems have been designed, image enhancement used or not, among other factors. To improve the accuracy of the image super resolution, a different deep learning techniques might be explored. In fact, greater research into the image super resolution in medical imaging is possible to improve the data's suitability for future analysis. In light of this, it can be said that there is a lot of scope for research in the field of medical imaging.

Article Details

How to Cite
R. Mhatre, S. ., & W. Bakal, J. . (2023). A Review of Image Super Resolution using Deep Learning. International Journal on Recent and Innovation Trends in Computing and Communication, 11(5s), 145–149. https://doi.org/10.17762/ijritcc.v11i5s.6638
Issue
Vol. 11 No. 5s (2023): Special Issue on Advances in Computing Systems for Web-Based Machine Learning Applications
Section
Review Paper

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