Deep Unfolding Network for Efficient Mixed Video Noise Removal-Reference-Cited by-同舟云学术

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Deep Unfolding Network for Efficient Mixed Video Noise Removal

Published:2023-09 Issue:9 Volume:33 Page:4715-4727
ISSN:1051-8215
Container-title:IEEE Transactions on Circuits and Systems for Video Technology
language:
Short-container-title:IEEE Trans. Circuits Syst. Video Technol.

Author:

Sun Lu¹,Wang Yichen¹^ORCID,Wu Fangfang²^ORCID,Li Xin³^ORCID,Dong Weisheng¹^ORCID,Shi Guangming¹^ORCID

Affiliation:

1. School of Artificial Intelligence, Xidian University, Xi’an, China

2. School of Computer Science and Technology, Xidian University, Xi’an, China

3. Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV, USA

Funder

Natural Science Foundation of China

NSF

West Virginia (WV) Higher Education Policy Commission Grant

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Media Technology

Link

https://ieeexplore.ieee.org/ielam/76/10241245/10050095-aam.pdf

Reference101 articles.

1. Restoration of images corrupted by Gaussian and uniform impulsive noise

2. Fast Two-Phase Image Deblurring Under Impulse Noise

3. DeGAN: Mixed noise removal via generative adversarial networks

4. An Adaptive Method for Recovering Image from Mixed Noisy Data

5. Restoration of Images Corrupted by Impulse Noise and Mixed Gaussian Impulse Noise Using Blind Inpainting

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