Comparative Study of Implementation of Very Deep Super Resolution Neural Network and Bicubic Interpolation for Single Image Super Resolution Quality Enhancement-Reference-Cited by-同舟云学术

Comparative Study of Implementation of Very Deep Super Resolution Neural Network and Bicubic Interpolation for Single Image Super Resolution Quality Enhancement

Published:2023-11-24 Issue: Volume:9 Page:1-7
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Container-title:2023 IEEE 3rd International Conference on Applied Electromagnetics, Signal Processing, & Communication (AESPC)
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Author:

P Ganesan¹^ORCID,Ravichandran M.²,Sathish B.S.³,Joseph L.M.I.Leo⁴,Sajiv G.⁵,Murugesan R.⁶

Affiliation:

1. Vidya Jyothi Institute of Technology,Dept. of Electronics and Communication Engineering,Hyderabad,India

2. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology,Dept. of Computer Science Engineering,Chennai,India

3. Sri Venkateshwara College of Engineering,Dept. of Computer Science Engineering AI,Bengaluru,India

4. SR University,Dept. of Electronics and Communication Engineering,Warangal,Telangana,India

5. Saveetha School of Engineering, SIMATS,Chennai,India

6. Narshima Reddy Engineering College,Dept. of Electronics and Communication Engineering,Secunderabad,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10389813/10389825/10389860.pdf?arnumber=10389860

Reference22 articles.

1. Lightweight Asymmetric Convolutional Distillation Network for Single Image Super-Resolution

2. Image super-resolution survey

3. A Performance Comparison of Super Resolution Model with Different Activation Functions;Yoo;KIPS Transactions on Software and Data Engineering,2020

4. VolumeNet: A Lightweight Parallel Network for Super-Resolution of MR and CT Volumetric Data

5. TDPN: Texture and Detail-Preserving Network for Single Image Super-Resolution