Multi-FusNet of Cross Channel Network for Image Super-Resolution-Reference-Cited by-同舟云学术

Multi-FusNet of Cross Channel Network for Image Super-Resolution

Published:2023 Issue: Volume:11 Page:56287-56299
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Ruangsang Watchara¹,Aramvith Supavadee¹^ORCID,Onoye Takao²^ORCID

Affiliation:

1. Department of Electrical Engineering, Faculty of Engineering, Multimedia Data Analytics and Processing Research Unit, Chulalongkorn University, Bangkok, Thailand

2. Graduate School of Information Science and Technology, Osaka University, Suita, Japan

Funder

The 90th Anniversary of Chulalongkorn University Fund (Ratchadaphiseksomphot Endowment Fund), Thailand Science Research and Innovation Fund Chulalongkorn University

The NSRF via the Program Management Unit for Human Resources & Institutional Development, Research and Innovation

Thailand Science research and Innovation Fund Chulalongkorn University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10143181.pdf?arnumber=10143181

Reference52 articles.

1. Deep Learning for Image Super-Resolution: A Survey

2. Multi-Scale Inception Based Super-Resolution Using Deep Learning Approach

3. Image Super-Resolution Using Deep Convolutional Networks

4. Learning a Deep Convolutional Network for Image Super-Resolution

5. Multi-residual networks: Improving the speed and accuracy of residual networks;abdi;arXiv 1609 05672 [cs],2016

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