An Iterative Mean Filter for Image Denoising

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dc.authoridEnginoglu, Serdar/0000-0002-7188-9893
dc.authorid, Le Minh Hieu/0000-0001-5252-199X
dc.authoridThanh, Dang/0000-0003-2025-8319
dc.authoridErkan, Ugur/0000-0002-2481-0230
dc.contributor.authorErkan, Ugur
dc.contributor.authorDang Ngoc Hoang Thanh
dc.contributor.authorLe Minh Hieu
dc.contributor.authorEnginoglu, Serdar
dc.date.accessioned2025-01-27T20:38:50Z
dc.date.available2025-01-27T20:38:50Z
dc.date.issued2019
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractWe propose an Iterative Mean Filter (IMF) to eliminate the salt-and-pepper noise. IMF uses the mean of gray values of noise-free pixels in a fixed-size window. Unlike other nonlinear filters, IMF does not enlarge the window size. A large size reduces the accuracy of noise removal. Therefore, IMF only uses a window with a size of 3 x 3. This feature is helpful for IMF to be able to more precisely evaluate a new gray value for the center pixel. To process high-density noise effectively, we propose an iterative procedure for IMF. In the experiments, we operationalize Peak Signal-to-Noise Ratio (PSNR), Visual Information Fidelity, Image Enhancement Factor, Structural Similarity (SSIM), and Multiscale Structure Similarity to assess image quality. Furthermore, we compare denoising results of IMF with ones of the other state-of-the-art methods. A comprehensive comparison of execution time is also provided. The qualitative results by PSNR and SSIM showed that IMF outperforms the other methods such as Based-on Pixel Density Filter (BPDF), Decision-Based Algorithm (DBA), Modified Decision-Based Untrimmed Median Filter (MDBUTMF), Noise Adaptive Fuzzy Switching Median Filter (NAFSMF), Adaptive Weighted Mean Filter (AWMF), Different Applied Median Filter (DAMF), Adaptive Type-2 Fuzzy Filter (FDS): for the IMAGESTEST dataset - BPDF (25.36/0.756), DBA (28.72/0.8426), MDBUTMF (25.93/0.8426), NAFSMF (29.32/0.8735), AWMF (32.25/0.9177), DAMF (31.65/0.9154), FDS (27.98/0.8338), and IMF (33.67/0.9252); and for the BSDS dataset - BPDF (24.95/0.7469), DBA (26.84/0.8061), MDBUTMF (26.25/0.7732), NAFSMF (27.26/0.8191), AWMF (28.89/0.8672), DAMF (29.11/0.8667), FDS (26.85/0.8095), and IMF (30.04/0.8753).
dc.identifier.doi10.1109/ACCESS.2019.2953924
dc.identifier.endpage167859
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85077201660
dc.identifier.scopusqualityQ1
dc.identifier.startpage167847
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2019.2953924
dc.identifier.urihttps://hdl.handle.net/20.500.12428/23774
dc.identifier.volume7
dc.identifier.wosWOS:000509585900136
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIEEE-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIeee Access
dc.relation.publicationcategoryinfo:eu-repo/semantics/openAccess
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20250125
dc.subjectSalt-and-pepper noise
dc.subjectimage denoising
dc.subjectnoise removal
dc.subjectimage restoration
dc.subjectimage processing
dc.subjectnonlinear filter
dc.titleAn Iterative Mean Filter for Image Denoising
dc.typeArticle

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