文献类型：期刊文献

英文题名：An improved fractional-order differentiation model for image denoising

作者：He, Ning[1];Wang, Jin-Bao[1];Zhang, Lu-Lu[1];Lu, Ke[2]

第一作者：何宁

通讯作者：He, N[1]

机构：[1]Beijing Union Univ, Coll Informat Technol, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China;[2]Univ Chinese Acad Sci, Beijing 100049, Peoples R China

第一机构：北京联合大学北京市信息服务工程重点实验室|北京联合大学智慧城市学院

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Informat Technol, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China.|[1141734]北京联合大学智慧城市学院;[11417]北京联合大学;[11417103]北京联合大学北京市信息服务工程重点实验室;

年份：2015

卷号：112

起止页码：180-188

外文期刊名：SIGNAL PROCESSING

收录：;EI(收录号：20150900587013);Scopus(收录号：2-s2.0-84923543490);WOS:【SCI-EXPANDED(收录号:WOS:000351976400019)】；

基金：This work was supported by the National Natural Science Foundation of China (Nos. 61370138, 61103130, 61271435, and U1301251); the Beijing Municipal Natural Science Foundation (No. 4141003); the National Program on Key Basic Research Projects (973 programs) (Grant nos. 2010CB731804-1 and 2011CB706901-4); the Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions (No. IDHT20130225); and the Construction of Innovative Teams and Teacher Career Development for Universities and Colleges under Beijing Municipality (No. CIT&TCD20130513).

语种：英文

外文关键词：Fractional order differentiation; Image denoising; Detailed features; Information entropy; Average gradient

摘要：This paper investigates fractional order differentiation and its applications in digital image processing. We propose an improved model based on the Grunwald-Letnikov (G-L) fractional differential operator. Our improved denoising operator mask is based on G-L fractional order differentiation. The total coefficient of this mask is not equal to zero, which means that its response value is not zero in flat areas of the image. This nonlinear filter mask enhances and preserves detailed features while effectively denoising the image. Our experiments on texture-rich digital images demonstrated the capabilities of the filter. We used the information entropy and average gradient to quantitatively compare our method to existing techniques. Additionally, we have successfully used it to denoise three-dimensional magnetic resonance images. (C) 2014 Elsevier B.V. All rights reserved.