An Iterative Mean Filter for Image Denoising
We 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 onl...
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| Format: | Article |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8903303/ |
| _version_ | 1819171289874563072 |
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| author | ugur erkan Dang Ngoc Hoang Thanh Le Minh Hieu Serdar Enginoglu |
| author_facet | ugur erkan Dang Ngoc Hoang Thanh Le Minh Hieu Serdar Enginoglu |
| author_sort | ugur erkan |
| collection | DOAJ |
| description | We 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 $3\times3$ . 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). |
| first_indexed | 2024-12-22T19:48:56Z |
| format | Article |
| id | doaj.art-68982d50c70e4e25a2e7cc774d007ba3 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T19:48:56Z |
| publishDate | 2019-01-01 |
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| spelling | doaj.art-68982d50c70e4e25a2e7cc774d007ba32022-12-21T18:14:36ZengIEEEIEEE Access2169-35362019-01-01716784716785910.1109/ACCESS.2019.29539248903303An Iterative Mean Filter for Image Denoisingugur erkan0https://orcid.org/0000-0002-2481-0230Dang Ngoc Hoang Thanh1https://orcid.org/0000-0003-2025-8319Le Minh Hieu2https://orcid.org/0000-0001-5252-199XSerdar Enginoglu3https://orcid.org/0000-0002-7188-9893Department of Computer Engineering, Faculty of Engineering, Karamanoğlu Mehmetbey University, Karaman, TurkeyDepartment of Information Technology, Hue College of Industry, Hue, VietnamDepartment of Economics, University of Economics - The University of Da Nang, Da Nang, VietnamDepartment of Mathematics, Faculty of Arts and Sciences, Çanakkale Onsekiz Mart University, Çanakkale, TurkeyWe 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 $3\times3$ . 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).https://ieeexplore.ieee.org/document/8903303/Salt-and-pepper noiseimage denoisingnoise removalimage restorationimage processingnonlinear filter |
| spellingShingle | ugur erkan Dang Ngoc Hoang Thanh Le Minh Hieu Serdar Enginoglu An Iterative Mean Filter for Image Denoising IEEE Access Salt-and-pepper noise image denoising noise removal image restoration image processing nonlinear filter |
| title | An Iterative Mean Filter for Image Denoising |
| title_full | An Iterative Mean Filter for Image Denoising |
| title_fullStr | An Iterative Mean Filter for Image Denoising |
| title_full_unstemmed | An Iterative Mean Filter for Image Denoising |
| title_short | An Iterative Mean Filter for Image Denoising |
| title_sort | iterative mean filter for image denoising |
| topic | Salt-and-pepper noise image denoising noise removal image restoration image processing nonlinear filter |
| url | https://ieeexplore.ieee.org/document/8903303/ |
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