Uncovering the Over-Smoothing Challenge in Image Super-Resolution: Entropy-Based Quantification and Contrastive Optimization-Reference-Cited by-同舟云学术

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Uncovering the Over-Smoothing Challenge in Image Super-Resolution: Entropy-Based Quantification and Contrastive Optimization

Published:2024-09 Issue:9 Volume:46 Page:6199-6215
ISSN:0162-8828
Container-title:IEEE Transactions on Pattern Analysis and Machine Intelligence
language:
Short-container-title:IEEE Trans. Pattern Anal. Mach. Intell.

Author:

Xu Tianshuo¹^ORCID,Li Lijiang¹,Mi Peng¹^ORCID,Zheng Xiawu²^ORCID,Chao Fei¹^ORCID,Ji Rongrong¹^ORCID,Tian Yonghong²^ORCID,Shen Qiang³^ORCID

Affiliation:

1. Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, School of Informatics, Xiamen University, Xiamen, China

2. Peng Cheng Laboratory, Shenzhen, China

3. United Kingdom Royal Academy of Engineering, London, U.K.

Funder

National Key R&D Program of China

National Science Fund for Distinguished Young Scholars

National Natural Science Foundation of China

Natural Science Foundation of Fujian Province of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/34/10627928/10475558.pdf?arnumber=10475558

Reference57 articles.

1. Super-Resolution in Medical Imaging

2. Simultaneous Super-Resolution and Cross-Modality Synthesis of 3D Medical Images Using Weakly-Supervised Joint Convolutional Sparse Coding

3. A super-resolution reconstruction algorithm for surveillance images

4. Convolutional Neural Network Super Resolution for Face Recognition in Surveillance Monitoring

5. Image Super-Resolution Using Deep Convolutional Networks

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