Optimum Median Filter Based on Crow Optimization Algorithm
A novel median filter based on crow optimization algorithms (OMF) is suggested to reduce the random salt and pepper noise and improve the quality of the RGB-colored and gray images. The fundamental idea of the approach is that first, the crow optimization algorithm detects noise pixels, and that rep...
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| Format: | Article |
| Language: | Arabic |
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College of Science for Women, University of Baghdad
2021-09-01
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| Series: | Baghdad Science Journal |
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| Online Access: | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/4525 |
| _version_ | 1818390699169546240 |
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| author | Basma Jumaa Saleh Ahmed Yousif Falih Saedi Ali Talib Qasim al-Aqbi Lamees abdalhasan Salman |
| author_facet | Basma Jumaa Saleh Ahmed Yousif Falih Saedi Ali Talib Qasim al-Aqbi Lamees abdalhasan Salman |
| author_sort | Basma Jumaa Saleh |
| collection | DOAJ |
| description | A novel median filter based on crow optimization algorithms (OMF) is suggested to reduce the random salt and pepper noise and improve the quality of the RGB-colored and gray images. The fundamental idea of the approach is that first, the crow optimization algorithm detects noise pixels, and that replacing them with an optimum median value depending on a criterion of maximization fitness function. Finally, the standard measure peak signal-to-noise ratio (PSNR), Structural Similarity, absolute square error and mean square error have been used to test the performance of suggested filters (original and improved median filter) used to removed noise from images. It achieves the simulation based on MATLAB R2019b and the results present that the improved median filter with crow optimization algorithm is more effective than the original median filter algorithm and some recently methods; they show that the suggested process is robust to reduce the error problem and remove noise because of a candidate of the median filter; the results will show by the minimized mean square error to equal or less than (1.38), absolute error to equal or less than (0.22) ,Structural Similarity (SSIM) to equal (0.9856) and getting PSNR more than (46 dB). Thus, the percentage of improvement in work is (25%). |
| first_indexed | 2024-12-14T05:01:46Z |
| format | Article |
| id | doaj.art-e5fce8a015dd48de9bf58a104d4c59d6 |
| institution | Directory Open Access Journal |
| issn | 2078-8665 2411-7986 |
| language | Arabic |
| last_indexed | 2024-12-14T05:01:46Z |
| publishDate | 2021-09-01 |
| publisher | College of Science for Women, University of Baghdad |
| record_format | Article |
| series | Baghdad Science Journal |
| spelling | doaj.art-e5fce8a015dd48de9bf58a104d4c59d62022-12-21T23:16:13ZaraCollege of Science for Women, University of BaghdadBaghdad Science Journal2078-86652411-79862021-09-0118310.21123/bsj.2021.18.3.0614Optimum Median Filter Based on Crow Optimization AlgorithmBasma Jumaa Saleh0Ahmed Yousif Falih Saedi1Ali Talib Qasim al-Aqbi2Lamees abdalhasan Salman3Computer Engineering Department, College of Engineering, Al-Mustansiriyah University, Baghdad, Iraq.Computer Engineering Department, College of Engineering, Al-Mustansiriyah University, Baghdad, Iraq.Computer Engineering Department, College of Engineering, Al-Mustansiriyah University, Baghdad, Iraq.Computer Engineering Department, College of Engineering, Al-Mustansiriyah University, Baghdad, Iraq.A novel median filter based on crow optimization algorithms (OMF) is suggested to reduce the random salt and pepper noise and improve the quality of the RGB-colored and gray images. The fundamental idea of the approach is that first, the crow optimization algorithm detects noise pixels, and that replacing them with an optimum median value depending on a criterion of maximization fitness function. Finally, the standard measure peak signal-to-noise ratio (PSNR), Structural Similarity, absolute square error and mean square error have been used to test the performance of suggested filters (original and improved median filter) used to removed noise from images. It achieves the simulation based on MATLAB R2019b and the results present that the improved median filter with crow optimization algorithm is more effective than the original median filter algorithm and some recently methods; they show that the suggested process is robust to reduce the error problem and remove noise because of a candidate of the median filter; the results will show by the minimized mean square error to equal or less than (1.38), absolute error to equal or less than (0.22) ,Structural Similarity (SSIM) to equal (0.9856) and getting PSNR more than (46 dB). Thus, the percentage of improvement in work is (25%).https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/4525Image processing, Impulse noise, Noise removal, Optimum median filter, Crow optimization algorithm. |
| spellingShingle | Basma Jumaa Saleh Ahmed Yousif Falih Saedi Ali Talib Qasim al-Aqbi Lamees abdalhasan Salman Optimum Median Filter Based on Crow Optimization Algorithm Baghdad Science Journal Image processing, Impulse noise, Noise removal, Optimum median filter, Crow optimization algorithm. |
| title | Optimum Median Filter Based on Crow Optimization Algorithm |
| title_full | Optimum Median Filter Based on Crow Optimization Algorithm |
| title_fullStr | Optimum Median Filter Based on Crow Optimization Algorithm |
| title_full_unstemmed | Optimum Median Filter Based on Crow Optimization Algorithm |
| title_short | Optimum Median Filter Based on Crow Optimization Algorithm |
| title_sort | optimum median filter based on crow optimization algorithm |
| topic | Image processing, Impulse noise, Noise removal, Optimum median filter, Crow optimization algorithm. |
| url | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/4525 |
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