Enhancing Upscaled Image Resolution Using Hybrid Generative Adversarial Network-Enabled Frameworks-Reference-Cited by-同舟云学术

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Enhancing Upscaled Image Resolution Using Hybrid Generative Adversarial Network-Enabled Frameworks

Published:2024 Issue: Volume:12 Page:27784-27793
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Geetha R.¹^ORCID,Jebamalar G. Belshia¹,Shiney S. Arumai¹^ORCID,Dao Nhu-Ngoc²^ORCID,Moon Hyeonjoon²^ORCID,Cho Sungrae³^ORCID

Affiliation:

1. Department of Computer Science and Engineering, S. A. Engineering College, Chennai, India

2. Department of Computer Science and Engineering, Sejong University, Seoul, South Korea

3. School of Computer Science and Engineering, Chung-Ang University, Seoul, South Korea

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10440335.pdf?arnumber=10440335

Reference35 articles.

1. Prediction of domestic power peak demand and consumption using supervised machine learning with smart meter dataset

2. Effective offline handwritten text recognition model based on a sequence-to-sequence approach with CNN–RNN networks

3. Generative adversarial network: An overview of theory and applications

4. Image Super Resolution Reconstruction Algorithm Based on Deep Learning

5. SwinIR: Image Restoration Using Swin Transformer

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