{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T21:54:26Z","timestamp":1740174866776,"version":"3.37.3"},"reference-count":14,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,6,7]],"date-time":"2021-06-07T00:00:00Z","timestamp":1623024000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11961012","71801060","2018GXNSFBA281192","2017GXNSFAA198248","2017GXNSFBA198182","61627807"],"award-info":[{"award-number":["11961012","71801060","2018GXNSFBA281192","2017GXNSFAA198248","2017GXNSFBA198182","61627807"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004607","name":"Natural Science Foundation of Guangxi Province","doi-asserted-by":"publisher","award":["11961012","71801060","2018GXNSFBA281192","2017GXNSFAA198248","2017GXNSFBA198182","61627807"],"award-info":[{"award-number":["11961012","71801060","2018GXNSFBA281192","2017GXNSFAA198248","2017GXNSFBA198182","61627807"]}],"id":[{"id":"10.13039\/501100004607","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Major Scientific Research Instruments Development Program","award":["11961012","71801060","2018GXNSFBA281192","2017GXNSFAA198248","2017GXNSFBA198182","61627807"],"award-info":[{"award-number":["11961012","71801060","2018GXNSFBA281192","2017GXNSFAA198248","2017GXNSFBA198182","61627807"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Scientific Programming"],"published-print":{"date-parts":[[2021,6,7]]},"abstract":"<jats:p>Medical image information may be polluted by noise in the process of generation and transmission, which will seriously hinder the follow-up image processing and medical diagnosis. In medical images, there is a typical mixed noise composed of additive white Gaussian noise (AWGN) and impulse noise. In the conventional denoising methods, impulse noise is first removed, followed by the elimination of white Gaussian noise (WGN). However, it is difficult to separate the two kinds of noises completely in practical application. The existing denoising algorithm of weight coding based on sparse nonlocal regularization, which can simultaneously remove AWGN and impulse noise, is plagued by the problems of incomplete noise removal and serious loss of details. The denoising algorithm based on sparse representation and low rank constraint can preserve image details better. Thus, a medical image denoising algorithm based on sparse nonlocal regularization weighted coding and low rank constraint is proposed. The denoising effect of the proposed method and the original algorithm on computed tomography (CT) image and magnetic resonance (MR) image are compared. It is revealed that, under different \u03c3 and \u03c1 values, the PSNR and FSIM values of CT and MRI images are evidently superior to those of traditional algorithms, suggesting that the algorithm proposed in this work has better denoising effects on medical images than traditional denoising algorithms.<\/jats:p>","DOI":"10.1155\/2021\/7008406","type":"journal-article","created":{"date-parts":[[2021,6,7]],"date-time":"2021-06-07T21:42:02Z","timestamp":1623102122000},"page":"1-6","source":"Crossref","is-referenced-by-count":0,"title":["Medical Image Denoising Algorithm Based on Sparse Nonlocal Regularized Weighted Coding and Low Rank Constraint"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8057-7867","authenticated-orcid":true,"given":"Quan","family":"Yuan","sequence":"first","affiliation":[{"name":"School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, Guangxi, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1454-786X","authenticated-orcid":true,"given":"Zhenyun","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, Guangxi, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1078-8455","authenticated-orcid":true,"given":"Zhencheng","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, Guangxi, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6121-7156","authenticated-orcid":true,"given":"Yanke","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, Guangxi, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5690-4533","authenticated-orcid":true,"given":"Bin","family":"Yang","sequence":"additional","affiliation":[{"name":"Xi\u2019an Tapo Primary School, Chang\u2019an District, Xi\u2019an 710119, Shaanxi, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8875-1989","authenticated-orcid":true,"given":"Xiangyan","family":"Zeng","sequence":"additional","affiliation":[{"name":"School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin 541004, Guangxi, China"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1109\/tmi.2004.832656"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.5815\/ijigsp.2012.02.08"},{"key":"3","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511609657","volume-title":"Digital Image Processing for Medical applications","author":"G. 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