SALT AND PEPPER NOISE FILTERING USING FUZZY LOGIC IN GRAYSCALE IMAGES
Keywords:
Impulse noise, Noise filtering, Fuzzy logic, Noise detection
Abstract
Digital image processing plays a pivotal role in various fields, from medical imaging to surveillance systems. However, the acquired images are often susceptible to various types of noise, such in the form of salt and pepper noise, which can severely degrade image quality and hinder subsequent analysis.In this study, we introduce a fuzzy impulse noise removal algorithm as a potential solution. The efficiency of the suggested algorithms is assessed by comparing their performance to various existing noise removal methods. Objective measurements, including peak signal-to-noise ratio and mean square error, are used to evaluate the results. The findings demonstrate that the proposed algorithms deliver excellent outcomes in noise reduction and image detail preservation across a broad range of noise densities.
References
1. Tong-Li He and Jian-Hong Gan, "A new method of removing salt-and-pepper noise basing on grey system model in images," 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems, Xiamen, China, 2010, pp. 574-576, doi: 10.1109/ICICISYS.2010.5658446.
2. R. R. Chand, M. Farik and N. A. Sharma, "Digital Image Processing Using Noise Removal Technique: A Non-Linear Approach," 2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), Gold Coast, Australia, 2022, pp. 1-5, doi: 10.1109/CSDE56538.2022.10089258.
3. K. Mahboob, S. Khursheed, S. M. Jameel, V. Uddin, S. Shukla and J. K. Pabani, "A Novel Medical Image De-noising Algorithm for Efficient Diagnosis in Smart Health Environment," 2022 Global Conference on Wireless and Optical Technologies (GCWOT), Malaga, Spain, 2022, pp. 1-5, doi: 10.1109/GCWOT53057.2022.9772907.
4. R. R. Chand, M. Farik and N. A. Sharma, "Digital Image Processing Using Noise Removal Technique: A Non-Linear Approach," 2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), Gold Coast, Australia, 2022, pp. 1-5, doi: 10.1109/CSDE56538.2022.10089258.
5. M. M. Hamid, F. Fathi Hammad and N. Hmad, "Removing the Impulse Noise from Grayscaled and Colored Digital Images Using Fuzzy Image Filtering," 2021 IEEE 1st International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering MI-STA, 2021, pp. 711-716.
6. M. Rakhshanfar and M. A. Amer, "Low-Frequency Image Noise Removal Using White Noise Filter," 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece, 2018, pp. 3948-3952, doi: 10.1109/ICIP.2018.8451391.
7. M. Shajahan, S. A. M. Aris, S. Usman and N. M. Noor, "IRPMID: Medical XRAY Image Impulse Noise Removal using Partition Aided Median, Interpolation and DWT," 2021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), Kuala Terengganu, Malaysia, 2021, pp. 105-110, doi: 10.1109/ICSIPA52582.2021.9576773.
8. C. Anjanappa and H. S. Sheshadri, "Development of mathematical morphology filter for medical image impulse noise removal," 2015 International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT), Mandya, India, 2015, pp. 311-318, doi: 10.1109/ERECT.2015.7499033.
9. H. K. Aggarwal, S. Tariyal and A. Majumdar, "Compressive hyper-spectral imaging in the presence of impulse noise," 2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Tokyo, Japan, 2015, pp. 1-4, doi: 10.1109/WHISPERS.2015.8075396.
10. T. M. Y. Shiju and A. V. N. Krishna, "A Two-Pass Hybrid Mean and Median Framework for Eliminating Impulse Noise From a Grayscale Image," 2021 2nd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS), Ernakulam, India, 2021, pp. 206-210, doi: 10.1109/ACCESS51619.2021.9563285.
11. A. Konieczka, J. Balcerek and A. Dąbrowski, "Method of adaptive pixel averaging for impulse noise reduction in digital images," 2018 Baltic URSI Symposium (URSI), Poznan, Poland, 2018, pp. 221-224, doi: 10.23919/URSI.2018.8406738.
12. Y. Jiang, "A Truncated L1-L2 Total Variational Method for Image Restoration with Impulse Noise," 2022 3rd International Conference on Computer Vision, Image and Deep Learning & International Conference on Computer Engineering and Applications (CVIDL & ICCEA), Changchun, China, 2022, pp. 1-4, doi: 10.1109/CVIDLICCEA56201.2022.9824196.
13. Y. Jiang, "A Truncated L1-L2 Total Variational Method for Image Restoration with Impulse Noise," 2022 3rd International Conference on Computer Vision, Image and Deep Learning & International Conference on Computer Engineering and Applications (CVIDL & ICCEA), Changchun, China, 2022, pp. 1-4, doi: 10.1109/CVIDLICCEA56201.2022.9824196.
14. P. Luo, X. Zhang, Z. Chang and W. Liu, "Research on Salt and Pepper Noise Removal Method Based on Adaptive Fuzzy Median Filter," 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 2021, pp. 387-392
15. G. P. Deepti, M. V. Borker and J. Sivaswamy, "Impulse Noise Removal from Color Images with Hopfield Neural Network and Improved Vector Median Filter," 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing, Bhubaneswar, India, 2008, pp. 17-24, doi: 10.1109/ICVGIP.2008.75.
16. X. Lin, L. Tian, Q. Du and C. Qin, "Improved Decision Based Adaptive Threshold Median Filter for Fingerprint Image Salt and Pepper Noise Denoising," 2022 21st International Symposium on Communications and Information Technologies (ISCIT), Xi'an, China, 2022, pp. 233-237, doi: 10.1109/ISCIT55906.2022.9931257.
17. Xiao Kang, Wei Zhu, KeJie Li and Jing Jiang, "A Novel Adaptive Switching Median filter for laser image based on local salt and pepper noise density," 2011 IEEE Power Engineering and Automation Conference, Wuhan, 2011, pp. 38-41, doi: 10.1109/PEAM.2011.6135010.
18. Z. Liu et al., "Fuzzy Logic-Based Adaptive Point Cloud Video Streaming," in IEEE Open Journal of the Computer Society, vol. 1, pp. 121-130, 2020, doi: 10.1109/OJCS.2020.3006205.
19. P. V. S. Reddy, "Generalized Fuzzy Logic with twofold fuzzy set: Learning through Neural Net and Application to Business Intelligence," 2021 International Conference on Fuzzy Theory and Its Applications (iFUZZY), 2021, pp. 1-5.
20. Muna M. Jawad, Ekbal H. Ali, and Adel J. Yousif, A Fuzzy Random Impulse Noise Detection and Reduction Method Based on Noise Density Estimation, International Journal of Scientific & Engineering Research, Volume 5, Issue 3, March 2014, pp. 455-468.
21. H. M. Rehan Afzal, J. Yu and Y. Kang, "Impulse noise removal using fuzzy logics," 2017 32nd Youth Academic Annual Conference of Chinese Association of Automation (YAC), Hefei, China, 2017, pp. 413-418, doi: 10.1109/YAC.2017.7967444.
22. Younghun Song, Yunsang Han and Sangkeun Lee, "Pixel Correlation-based Impulse Noise Reduction," 2011 17th Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV), Ulsan, 2011, pp. 1-4, doi: 10.1109/FCV.2011.5739722.
23. M. E. Mathew and J. Jeevitha, "An impulse noise cancellation using iterative algorithms," 2014 International Conference on Electronics and Communication Systems (ICECS), Coimbatore, India, 2014, pp. 1-6, doi: 10.1109/ECS.2014.6892579.
24. P. A. Lyakhov, A. S. Voznesensky, E. D. Shalugin, A. R. Orazaev and V. A. Baboshina, "Bilateral and Median Filter Combination for High-Quality Cleaning of Random Impulse Noise in Images," 2022 11th Mediterranean Conference on Embedded Computing (MECO), Budva, Montenegro, 2022, pp. 1-5, doi: 10.1109/MECO55406.2022.9797149.
25. W. Luo, “Efficient Removal of Impulse Noise from Digital Images”, IEEE Transactions Consumer Electronics, vol. (52), No. (2), pp. (523-527), May, 2006.
26. T. Hasuike and H. Katagiri, "A Subjective and Objective Constructing Approach for Reasonable Membership Function Based on Mathematical Programming," 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems (SCIS) and 17th International Symposium on Advanced Intelligent Systems (ISIS), Sapporo, Japan, 2016, pp. 59-64, doi: 10.1109/SCIS-ISIS.2016.0026.
27. P. Biswas and K. K. Halder, "Speckle Noise Reduction from Medical Images Using Gaussian Fuzzy Membership Function," 2021 3rd International Conference on Electrical & Electronic Engineering (ICEEE), Rajshahi, Bangladesh, 2021, pp. 41-44, doi: 10.1109/ICEEE54059.2021.9718944.
28. A. Kumar, T. Sharma, N. K. Verma, P. Sircar and S. Vasikarla, "Detection and Removal of Salt and Pepper Noise by Gaussian Membership Function and Guided Filter," 2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), Washington, DC, USA, 2019, pp. 1-9, doi: 10.1109/AIPR47015.2019.9174579.
29. L. Li, P. Cao, J. Yang, D. Zhao and O. R. Zaiane, "A robust fuzzy clustering algorithm using spatial information combined with local membership filtering for brain MR images," 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Seoul, Korea (South), 2020, pp. 1987-1994, doi: 10.1109/BIBM49941.2020.9313402.
30. M. Kumar and B. Freudenthaler, "Fuzzy Membership Functional Analysis for Nonparametric Deep Models of Image Features," in IEEE Transactions on Fuzzy Systems, vol. 28, no. 12, pp. 3345-3359, Dec. 2020, doi: 10.1109/TFUZZ.2019.2950636.
2. R. R. Chand, M. Farik and N. A. Sharma, "Digital Image Processing Using Noise Removal Technique: A Non-Linear Approach," 2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), Gold Coast, Australia, 2022, pp. 1-5, doi: 10.1109/CSDE56538.2022.10089258.
3. K. Mahboob, S. Khursheed, S. M. Jameel, V. Uddin, S. Shukla and J. K. Pabani, "A Novel Medical Image De-noising Algorithm for Efficient Diagnosis in Smart Health Environment," 2022 Global Conference on Wireless and Optical Technologies (GCWOT), Malaga, Spain, 2022, pp. 1-5, doi: 10.1109/GCWOT53057.2022.9772907.
4. R. R. Chand, M. Farik and N. A. Sharma, "Digital Image Processing Using Noise Removal Technique: A Non-Linear Approach," 2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), Gold Coast, Australia, 2022, pp. 1-5, doi: 10.1109/CSDE56538.2022.10089258.
5. M. M. Hamid, F. Fathi Hammad and N. Hmad, "Removing the Impulse Noise from Grayscaled and Colored Digital Images Using Fuzzy Image Filtering," 2021 IEEE 1st International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering MI-STA, 2021, pp. 711-716.
6. M. Rakhshanfar and M. A. Amer, "Low-Frequency Image Noise Removal Using White Noise Filter," 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece, 2018, pp. 3948-3952, doi: 10.1109/ICIP.2018.8451391.
7. M. Shajahan, S. A. M. Aris, S. Usman and N. M. Noor, "IRPMID: Medical XRAY Image Impulse Noise Removal using Partition Aided Median, Interpolation and DWT," 2021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), Kuala Terengganu, Malaysia, 2021, pp. 105-110, doi: 10.1109/ICSIPA52582.2021.9576773.
8. C. Anjanappa and H. S. Sheshadri, "Development of mathematical morphology filter for medical image impulse noise removal," 2015 International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT), Mandya, India, 2015, pp. 311-318, doi: 10.1109/ERECT.2015.7499033.
9. H. K. Aggarwal, S. Tariyal and A. Majumdar, "Compressive hyper-spectral imaging in the presence of impulse noise," 2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Tokyo, Japan, 2015, pp. 1-4, doi: 10.1109/WHISPERS.2015.8075396.
10. T. M. Y. Shiju and A. V. N. Krishna, "A Two-Pass Hybrid Mean and Median Framework for Eliminating Impulse Noise From a Grayscale Image," 2021 2nd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS), Ernakulam, India, 2021, pp. 206-210, doi: 10.1109/ACCESS51619.2021.9563285.
11. A. Konieczka, J. Balcerek and A. Dąbrowski, "Method of adaptive pixel averaging for impulse noise reduction in digital images," 2018 Baltic URSI Symposium (URSI), Poznan, Poland, 2018, pp. 221-224, doi: 10.23919/URSI.2018.8406738.
12. Y. Jiang, "A Truncated L1-L2 Total Variational Method for Image Restoration with Impulse Noise," 2022 3rd International Conference on Computer Vision, Image and Deep Learning & International Conference on Computer Engineering and Applications (CVIDL & ICCEA), Changchun, China, 2022, pp. 1-4, doi: 10.1109/CVIDLICCEA56201.2022.9824196.
13. Y. Jiang, "A Truncated L1-L2 Total Variational Method for Image Restoration with Impulse Noise," 2022 3rd International Conference on Computer Vision, Image and Deep Learning & International Conference on Computer Engineering and Applications (CVIDL & ICCEA), Changchun, China, 2022, pp. 1-4, doi: 10.1109/CVIDLICCEA56201.2022.9824196.
14. P. Luo, X. Zhang, Z. Chang and W. Liu, "Research on Salt and Pepper Noise Removal Method Based on Adaptive Fuzzy Median Filter," 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 2021, pp. 387-392
15. G. P. Deepti, M. V. Borker and J. Sivaswamy, "Impulse Noise Removal from Color Images with Hopfield Neural Network and Improved Vector Median Filter," 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing, Bhubaneswar, India, 2008, pp. 17-24, doi: 10.1109/ICVGIP.2008.75.
16. X. Lin, L. Tian, Q. Du and C. Qin, "Improved Decision Based Adaptive Threshold Median Filter for Fingerprint Image Salt and Pepper Noise Denoising," 2022 21st International Symposium on Communications and Information Technologies (ISCIT), Xi'an, China, 2022, pp. 233-237, doi: 10.1109/ISCIT55906.2022.9931257.
17. Xiao Kang, Wei Zhu, KeJie Li and Jing Jiang, "A Novel Adaptive Switching Median filter for laser image based on local salt and pepper noise density," 2011 IEEE Power Engineering and Automation Conference, Wuhan, 2011, pp. 38-41, doi: 10.1109/PEAM.2011.6135010.
18. Z. Liu et al., "Fuzzy Logic-Based Adaptive Point Cloud Video Streaming," in IEEE Open Journal of the Computer Society, vol. 1, pp. 121-130, 2020, doi: 10.1109/OJCS.2020.3006205.
19. P. V. S. Reddy, "Generalized Fuzzy Logic with twofold fuzzy set: Learning through Neural Net and Application to Business Intelligence," 2021 International Conference on Fuzzy Theory and Its Applications (iFUZZY), 2021, pp. 1-5.
20. Muna M. Jawad, Ekbal H. Ali, and Adel J. Yousif, A Fuzzy Random Impulse Noise Detection and Reduction Method Based on Noise Density Estimation, International Journal of Scientific & Engineering Research, Volume 5, Issue 3, March 2014, pp. 455-468.
21. H. M. Rehan Afzal, J. Yu and Y. Kang, "Impulse noise removal using fuzzy logics," 2017 32nd Youth Academic Annual Conference of Chinese Association of Automation (YAC), Hefei, China, 2017, pp. 413-418, doi: 10.1109/YAC.2017.7967444.
22. Younghun Song, Yunsang Han and Sangkeun Lee, "Pixel Correlation-based Impulse Noise Reduction," 2011 17th Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV), Ulsan, 2011, pp. 1-4, doi: 10.1109/FCV.2011.5739722.
23. M. E. Mathew and J. Jeevitha, "An impulse noise cancellation using iterative algorithms," 2014 International Conference on Electronics and Communication Systems (ICECS), Coimbatore, India, 2014, pp. 1-6, doi: 10.1109/ECS.2014.6892579.
24. P. A. Lyakhov, A. S. Voznesensky, E. D. Shalugin, A. R. Orazaev and V. A. Baboshina, "Bilateral and Median Filter Combination for High-Quality Cleaning of Random Impulse Noise in Images," 2022 11th Mediterranean Conference on Embedded Computing (MECO), Budva, Montenegro, 2022, pp. 1-5, doi: 10.1109/MECO55406.2022.9797149.
25. W. Luo, “Efficient Removal of Impulse Noise from Digital Images”, IEEE Transactions Consumer Electronics, vol. (52), No. (2), pp. (523-527), May, 2006.
26. T. Hasuike and H. Katagiri, "A Subjective and Objective Constructing Approach for Reasonable Membership Function Based on Mathematical Programming," 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems (SCIS) and 17th International Symposium on Advanced Intelligent Systems (ISIS), Sapporo, Japan, 2016, pp. 59-64, doi: 10.1109/SCIS-ISIS.2016.0026.
27. P. Biswas and K. K. Halder, "Speckle Noise Reduction from Medical Images Using Gaussian Fuzzy Membership Function," 2021 3rd International Conference on Electrical & Electronic Engineering (ICEEE), Rajshahi, Bangladesh, 2021, pp. 41-44, doi: 10.1109/ICEEE54059.2021.9718944.
28. A. Kumar, T. Sharma, N. K. Verma, P. Sircar and S. Vasikarla, "Detection and Removal of Salt and Pepper Noise by Gaussian Membership Function and Guided Filter," 2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), Washington, DC, USA, 2019, pp. 1-9, doi: 10.1109/AIPR47015.2019.9174579.
29. L. Li, P. Cao, J. Yang, D. Zhao and O. R. Zaiane, "A robust fuzzy clustering algorithm using spatial information combined with local membership filtering for brain MR images," 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Seoul, Korea (South), 2020, pp. 1987-1994, doi: 10.1109/BIBM49941.2020.9313402.
30. M. Kumar and B. Freudenthaler, "Fuzzy Membership Functional Analysis for Nonparametric Deep Models of Image Features," in IEEE Transactions on Fuzzy Systems, vol. 28, no. 12, pp. 3345-3359, Dec. 2020, doi: 10.1109/TFUZZ.2019.2950636.
Published
2023-08-25
How to Cite
Ahmed, G. J. (2023). SALT AND PEPPER NOISE FILTERING USING FUZZY LOGIC IN GRAYSCALE IMAGES. CENTRAL ASIAN JOURNAL OF MATHEMATICAL THEORY AND COMPUTER SCIENCES, 4(8), 81-90. Retrieved from https://cajmtcs.centralasianstudies.org/index.php/CAJMTCS/article/view/502
Section
Articles
