Fuzzy Salt and Pepper Noise Removing for Color Images
Penghilang Noise untuk Gambar Berwarna menggunakan Fuzy Salt and Paper
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(1) * Adel Jalal
Iraq
(*) Corresponding Author
Abstract
Within the field of digital image processing applications, observed images frequently exposed to noise corruption stemming from image acquisition or transmission processes. This noise degradation reduces image quality and yields unfavorable outcomes in subsequent processing stages (e.g., segmentation, pattern recognition, and enhancement). Consequently, the mitigation of noise in images assumes paramount importance in the domain of image processing. This study introduces an algorithm centered around fuzzy logic for removing impulse noise from color images. The efficiency of the proposed algorithms is assessed by comparing their performance against various noise reduction methods. Objective metrics, namely peak signal-to-noise ratio and mean square error, substantiate that the proposed algorithms yield commendable outcomes in noise reduction and the preservation of intricate image details across a wide spectrum of noise densities
References
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[9] Dr. Muna M. Jawad, Dr. Ekbal H. Ali, 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
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[14] S. A. Narayanan, G. Arumugam, Prof. Kamal Bijlani, “Trimmed Median Filters for Salt and Pepper Noise Removal”, International Journal of Emerging Trends and Technology in Computer Science, Vol. (2), Issue (1), pp. (35-40) February, 2013.
[15] Adel Jalal Yousif, “A Discrete Cosine Transform Based Watermarking Scheme for Color Image Using YCbCr Space”, Journal of Engineering and Sustainable Development, vol. 22, issue 6, 2018.
[16] Adel Jalal Yousif, “Image Steganography Based on Wavelet Transform and Color Space Approach”, Diyala Journal of Engineering Sciences Vol (13) No 3, 2020: 23-34.
[17] Fadhil Kadhim Zaidan Ghazwan Jabbar Ahmed, Adel Jalal Yousif, “A Digital Image Watermarking Scheme based on Discrete Cosine Transform”, Journal of Engineering and Applied Sciences, vol. 4, issue 16, pp. 5762-5768, 2019.
[2] P. H. Sangave and G. P. Jain, "Impulse noise detection and removal by modified boundary discriminative noise detection technique," 2017 International Conference on Intelligent Sustainable Systems (ICISS), Palladam, India, 2017, pp. 715-719.
[3] M. Malekzadeh, S. Meshgini, R. Afrouzian, A. Farzamnia and S. Sheykhivand, "Removing mixture of Gaussian and Impulse noise of images using sparse coding," 2020 International Conference on Machine Vision and Image Processing (MVIP), Iran, 2020, pp. 1-4.
[4] 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.
[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, Tripoli, Libya, 2021, pp. 711-716.
[6] 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.
[7] 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), Taitung, Taiwan, 2021, pp. 1-5.
[8] N. Kamide, "Sequential Fuzzy Description Logic: Reasoning for Fuzzy Knowledge Bases with Sequential Information," 2020 IEEE 50th International Symposium on Multiple-Valued Logic (ISMVL), Miyazaki, Japan, 2020, pp. 218-223.
[9] Dr. Muna M. Jawad, Dr. Ekbal H. Ali, 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
[10] J. M. Mendel and D. Wu, "Critique of “A New Look at Type-2 Fuzzy Sets and Type-2 Fuzzy Logic Systems”," in IEEE Transactions on Fuzzy Systems, vol. 25, no. 3, pp. 725-727, June 2017, doi: 10.1109/TFUZZ.2017.2648882.
[11] W. Luo, “Efficient Removal of Impulse Noise from Digital Images”, IEEE Transactions Consumer Electronics, vol. (52), No. (2), pp. (523-527), May, 2006.
[12] J. Astola, P. Haavisto, Y. Neuvo, “Vector Median Filters”, Proceedings of the IEEE Vol. (78), No. (4), pp. (678–689), 1990.
[13] S. Schulte, V. De Witte, M. Nachtegael, D. Van Der Weken, E. E. Kerre, “Histogram-Based Fuzzy Colour Filter for Image Restoration”, Image and Vision Computing, Vol. (25), pp. (1377-1390), 2007.
[14] S. A. Narayanan, G. Arumugam, Prof. Kamal Bijlani, “Trimmed Median Filters for Salt and Pepper Noise Removal”, International Journal of Emerging Trends and Technology in Computer Science, Vol. (2), Issue (1), pp. (35-40) February, 2013.
[15] Adel Jalal Yousif, “A Discrete Cosine Transform Based Watermarking Scheme for Color Image Using YCbCr Space”, Journal of Engineering and Sustainable Development, vol. 22, issue 6, 2018.
[16] Adel Jalal Yousif, “Image Steganography Based on Wavelet Transform and Color Space Approach”, Diyala Journal of Engineering Sciences Vol (13) No 3, 2020: 23-34.
[17] Fadhil Kadhim Zaidan Ghazwan Jabbar Ahmed, Adel Jalal Yousif, “A Digital Image Watermarking Scheme based on Discrete Cosine Transform”, Journal of Engineering and Applied Sciences, vol. 4, issue 16, pp. 5762-5768, 2019.
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Published
2023-08-10
How to Cite
Jalal, A. (2023). Fuzzy Salt and Pepper Noise Removing for Color Images. JOINCS (Journal of Informatics, Network, and Computer Science), 6(1). https://doi.org/10.21070/joincs.v6i1.1623
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Copyright (c) 2023 Adel Jalal
This work is licensed under a Creative Commons Attribution 4.0 International License.