An Advanced AFWMF Model for Identifying High Random-Valued Impulse Noise for Image Processing
In this study, a novel adaptive fuzzy weighted mean filter (AFWMF) model based on the directional median technique and fuzzy inference is presented for solving the restoring high-ratio random-valued noise in image processing. This study aims, not only to obtain information from each direction of the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/15/7037 |
| _version_ | 1797525777226399744 |
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| author | Jieh-Ren Chang You-Shyang Chen Chih-Min Lo Huan-Chung Chen |
| author_facet | Jieh-Ren Chang You-Shyang Chen Chih-Min Lo Huan-Chung Chen |
| author_sort | Jieh-Ren Chang |
| collection | DOAJ |
| description | In this study, a novel adaptive fuzzy weighted mean filter (AFWMF) model based on the directional median technique and fuzzy inference is presented for solving the restoring high-ratio random-valued noise in image processing. This study aims, not only to obtain information from each direction of the filtering window, but also to gain information from every pixel of the filtering windows completely. Thus, in order to implement preserving details and textures for better restoration in high-noise cases, this study utilizes the directional median to build the membership function in fuzzy inference dynamically, then calculates the weighted window corresponding to the filtering window using fuzzy inference to represent the importance of valuable pixels. Finally, the restoration pixel is calculated using the weighted window and the filtering window for the weighted mean. Subsequently, this new AFWMF model significantly improves performances in the measurement of the peak signal to noise ratio (PSNR) value for preserving detail and fixed image in noise density within the range of 20–70% for the five well-known experimental images. In extensive experiments, this study also shows the better performance of identifying the proposed peak signal-to-removal noise ratio (PSRNR) and evaluating psycho-visual tests than other listed filter methods. Furthermore, the proposed AFWMF model also has a better structural similarity index measure (SSIM) value of another indicator. Conclusively, two interesting and meaning findings are identified: (1) the proposed AFWMF model is generally the best model among the 10 listed filtering methods for image processing in terms of the measurement of two quantitative indicators for both the PSNR and SSIM values; (2) different impulse noise densities should be made for different filtering methods, and thus, this is an important and interesting issue when aiming to identify an appropriate filtering model from a variety of images for processing various noise densities. |
| first_indexed | 2024-03-10T09:18:46Z |
| format | Article |
| id | doaj.art-5e57a5dad6f247dca74d8b93958620a2 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T09:18:46Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-5e57a5dad6f247dca74d8b93958620a22023-11-22T05:23:28ZengMDPI AGApplied Sciences2076-34172021-07-011115703710.3390/app11157037An Advanced AFWMF Model for Identifying High Random-Valued Impulse Noise for Image ProcessingJieh-Ren Chang0You-Shyang Chen1Chih-Min Lo2Huan-Chung Chen3Department of Electronic Engineering, National Ilan University, Yilan City 26047, TaiwanDepartment of Information Management, Hwa Hsia University of Technology, New Taipei City 23568, TaiwanDepartment of Digital Multimedia Design, National Taipei University of Business, Taipei City 100025, TaiwanDepartment of Electronic Engineering, National Ilan University, Yilan City 26047, TaiwanIn this study, a novel adaptive fuzzy weighted mean filter (AFWMF) model based on the directional median technique and fuzzy inference is presented for solving the restoring high-ratio random-valued noise in image processing. This study aims, not only to obtain information from each direction of the filtering window, but also to gain information from every pixel of the filtering windows completely. Thus, in order to implement preserving details and textures for better restoration in high-noise cases, this study utilizes the directional median to build the membership function in fuzzy inference dynamically, then calculates the weighted window corresponding to the filtering window using fuzzy inference to represent the importance of valuable pixels. Finally, the restoration pixel is calculated using the weighted window and the filtering window for the weighted mean. Subsequently, this new AFWMF model significantly improves performances in the measurement of the peak signal to noise ratio (PSNR) value for preserving detail and fixed image in noise density within the range of 20–70% for the five well-known experimental images. In extensive experiments, this study also shows the better performance of identifying the proposed peak signal-to-removal noise ratio (PSRNR) and evaluating psycho-visual tests than other listed filter methods. Furthermore, the proposed AFWMF model also has a better structural similarity index measure (SSIM) value of another indicator. Conclusively, two interesting and meaning findings are identified: (1) the proposed AFWMF model is generally the best model among the 10 listed filtering methods for image processing in terms of the measurement of two quantitative indicators for both the PSNR and SSIM values; (2) different impulse noise densities should be made for different filtering methods, and thus, this is an important and interesting issue when aiming to identify an appropriate filtering model from a variety of images for processing various noise densities.https://www.mdpi.com/2076-3417/11/15/7037fuzzy inferenceweighted windowdirectional medianweighted mean filterhigh noise |
| spellingShingle | Jieh-Ren Chang You-Shyang Chen Chih-Min Lo Huan-Chung Chen An Advanced AFWMF Model for Identifying High Random-Valued Impulse Noise for Image Processing Applied Sciences fuzzy inference weighted window directional median weighted mean filter high noise |
| title | An Advanced AFWMF Model for Identifying High Random-Valued Impulse Noise for Image Processing |
| title_full | An Advanced AFWMF Model for Identifying High Random-Valued Impulse Noise for Image Processing |
| title_fullStr | An Advanced AFWMF Model for Identifying High Random-Valued Impulse Noise for Image Processing |
| title_full_unstemmed | An Advanced AFWMF Model for Identifying High Random-Valued Impulse Noise for Image Processing |
| title_short | An Advanced AFWMF Model for Identifying High Random-Valued Impulse Noise for Image Processing |
| title_sort | advanced afwmf model for identifying high random valued impulse noise for image processing |
| topic | fuzzy inference weighted window directional median weighted mean filter high noise |
| url | https://www.mdpi.com/2076-3417/11/15/7037 |
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