UM  > Faculty of Science and Technology
Affiliated with RCfalse
Status已發表Published
SIMULTANEOUS NONLOCAL LOW-RANK AND DEEP PRIORS FOR POISSON DENOISING
Zha, Zhiyuan1; Wen, Bihan1; Yuan, Xin2; Zhou, Jiantao3; Zhu, Ce4
2022
Conference NameICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Source PublicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2022-May
Pages2320-2324
Conference Date23-27 May 2022
Conference PlaceSingapore, Singapore
Abstract

Poisson noise is a common electronic noise, which has widely occurred in various photo-limited imaging systems. However, due to signal-dependent and multiplicative characteristics for Poisson noise, Poisson denoising is still an open problem. In this paper, we propose a novel approach using simultaneous nonlocal low-rank and deep priors (SNLDP) for Poisson denoising. The proposed SNLDP simultaneously employs nonlocal self-similarity and deep image priors under the hybrid plug and play framework, which comprises multiple pairs of complementary priors, namely, nonlocal and local, shallow and deep, and internal and external. To make the optimization tractable, an effective alternating direction method of multiplier (ADMM) algorithm under the alternative minimization framework is provided to solve the proposed SNLDP-based Poisson denoising problem. Experimental results demonstrate the superiority of the proposed SNLDP over many popular or state-of-the-art Poisson denoising algorithms in terms of quantitative and visual perception.

KeywordDeep Prior Hybrid Plug And Play Nonlocal Self-similarity Optimization Poisson Denoising
DOI10.1109/ICASSP43922.2022.9746870
URLView the original
Language英語English
Scopus ID2-s2.0-85131248502
Fulltext Access
Citation statistics
Document TypeConference paper
CollectionFaculty of Science and Technology
Affiliation1.School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore
2.Nokia Bell Labs, Murray Hill, 600 Mountain Avenue, 07974, United States
3.Department of Computer and Information Science, University of Macau, 999078, Macao
4.School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
Recommended Citation
GB/T 7714
Zha, Zhiyuan,Wen, Bihan,Yuan, Xin,et al. SIMULTANEOUS NONLOCAL LOW-RANK AND DEEP PRIORS FOR POISSON DENOISING[C],2022:2320-2324.
APA Zha, Zhiyuan,Wen, Bihan,Yuan, Xin,Zhou, Jiantao,&Zhu, Ce.(2022).SIMULTANEOUS NONLOCAL LOW-RANK AND DEEP PRIORS FOR POISSON DENOISING.ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings,2022-May,2320-2324.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Zha, Zhiyuan]'s Articles
[Wen, Bihan]'s Articles
[Yuan, Xin]'s Articles
Baidu academic
Similar articles in Baidu academic
[Zha, Zhiyuan]'s Articles
[Wen, Bihan]'s Articles
[Yuan, Xin]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Zha, Zhiyuan]'s Articles
[Wen, Bihan]'s Articles
[Yuan, Xin]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.