Adaptive Fuzzy Filter Technique for Mixed Noise Removing from Sonar Images Underwater
DOI:
https://doi.org/10.59247/jfsc.v2i2.176Keywords:
Additive Noise, Impulse Noise (Salt & Pepper), Speckle Noise, Mixed Noise, Fuzzy Classical Filter, Nonlinear FilterAbstract
Underwater Analysis of acquired images may be affected by low contrast, haze, and other disturbances., caused by scattering and absorption of the light through propagation. An adaptive fuzzy filter for three mixed noise reduction is adopted on underwater sonar images to take out the various noises that either appear in the image when captured or injected into the image when transmitted. Underwater images when captured usually have speckle noise, salt, pepper noise also Gaussian noise. Is suggested in this paper an adaptive fuzzy filter structure that combines the fuzzy filter, sigmoid sliding control to minimize error as possible, and mean filter to reduce three mixed noises from sonar images underwater. This technique gives the best results especially with speckle noise compared to mean filter, median filter as no adaptive filters, and fuzzy filters, frost filter as adaptive filters. The MATLAB programs are adopted to simulate the proposed system.
References
G Kalpana, V Rajendran & S Sakthivel Murugan “Study of de-noising techniques for SNR improvement for underwater acoustic communication” Journal of Marine Engineering & Technology, vol. 13, no. 3, pp. 29–35, 2014, https://doi.org/10.1080/20464177.2014.11658119.
D. Chen, X. Chu, F. Ma and X. Teng, "A variational approach for adaptive underwater sonar image denoising," 2017 4th International Conference on Transportation Information and Safety (ICTIS), pp. 1177-1181, 2017, https://doi.org/10.1109/ICTIS.2017.8047920.
G. Saranya, K. Porkumaran and S. Prabakar, "Mixed noise removal of a color image using simple fuzzy filter," 2014 International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE), Coimbatore, India, 2014, pp. 1-6, 2014, https://doi.org/10.1109/ICGCCEE.2014.6922443.
B. B. Ahamed, D. Yuvaraj and S. S. Priya, "Image Denoising With Linear and Non-linear Filters," 2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), pp. 806-810, 2019, https://doi.org/10.1109/ICCIKE47802.2019.9004429.
L. Fan, F. Zhang, H. Fan, and C. Zhang, “Brief review of image denoising techniques,” Visual Computing for Industry, Biomedicine, and Art, vol. 2, no. 1, p. 7, 2019, https://doi.org/10.1186/s42492-019-0016-7.
J. Arnal and L. Súcar, “Hybrid filter based on fuzzy techniques for mixed noise reduction in color images,” Applied Sciences, vol. 10, no. 1, p. 243, 2019, https://doi.org/10.3390/app10010243.
Y. Huang, W. Li, and F. Yuan, “Speckle noise reduction in sonar image based on adaptive redundant dictionary,” Journal of marine science and engineering, vol. 8, no. 10, p. 761, 2020, https://doi.org/10.3390/jmse8100761.
Rui Li and Yu-Jin Zhang, "A hybrid filter for the cancellation of mixed Gaussian noise and impulse noise," Fourth International Conference on Information, Communications and Signal Processing, 2003 and the Fourth Pacific Rim Conference on Multimedia. Proceedings of the 2003 Joint, pp. 508-512 vol.1, 2003, https://doi.org/10.1109/ICICS.2003.1292504.
A. Caliskan, Z. A. Çil, H. Badem and D. Karaboga, "Regression-Based Neuro-Fuzzy Network Trained by ABC Algorithm for High-Density Impulse Noise Elimination," in IEEE Transactions on Fuzzy Systems, vol. 28, no. 6, pp. 1084-1095, 2020, https://doi.org/10.1109/TFUZZ.2020.2973123.
I. Abid, A. Boudabous and A. Ben Atitallah, "A new adaptive vector median rational hybrid filter for impulsive noise suppression," 2015 16th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), pp. 417-421, 2015, https://doi.org/10.1109/STA.2015.7505212.
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, pp. 711-716, 2021, https://doi.org/10.1109/MI-STA52233.2021.9464371.
S. Bharati, T. Z. Khan, P. Podder, and N. Q. Hung, “A comparative analysis of image denoising problem: noise models, denoising filters and applications,” Cognitive Internet of Medical Things for Smart Healthcare: Services and Applications, pp. 49-66, 2021, https://doi.org/10.1007/978-3-030-55833-8_3.
E. H. Ali, A. H. Reja, and L. H. Abood, “Design hybrid filter technique for mixed noise reduction from synthetic aperture radar imagery,” Bulletin of Electrical Engineering and Informatics, vol. 11, no. 3, pp. 1325-1331, 2022, https://doi.org/10.11591/eei.v11i3.3708.
Z. Hu, et al., “Artifact correction in low‐dose dental CT imaging using Wasserstein generative adversarial networks,” Medical physics, vol. 46, no. 4, pp. 1686-1696, 2019, https://doi.org/10.1002/mp.13415.
J. Chaki and N. Dey. A beginner’s guide to image preprocessing techniques. CRC Press. 2018. https://doi.org/10.1201/9780429441134.
J. Arnal and L. Súcar, “Hybrid filter based on fuzzy techniques for mixed noise reduction in color images,” Applied Sciences, vol. 10, no. 1, p. 243, 2019, https://doi.org/10.3390/app10010243.
R. K. Pearson, Y. Neuvo, J. Astola and M. Gabbouj, "The class of generalized hampel filters," 2015 23rd European Signal Processing Conference (EUSIPCO), pp. 2501-2505, 2015, https://doi.org/10.1109/EUSIPCO.2015.7362835.
S. Jian and W. Wen, “Study on underwater image denoising algorithm based on wavelet transform,” In Journal of Physics: Conference Series, vol. 806, no. 1, p. 012006, 2017, https://doi.org/10.1088/1742-6596/806/1/012006.
M. S. Nair and G. Raju, “Additive noise removal using a novel fuzzy-based filter,” Computers & Electrical Engineering, vol. 37, no. 5, pp. 644-655, 2011, https://doi.org/10.1007/978-90-481-2311-7_8.
M. Liu, Z. Zhou, P. Shang and D. Xu, "Fuzzified Image Enhancement for Deep Learning in Iris Recognition," in IEEE Transactions on Fuzzy Systems, vol. 28, no. 1, pp. 92-99, 2020, https://doi.org/10.1109/TFUZZ.2019.2912576.
L. H. Abood, B. K. Oleiwi, A. J. Humaidi, A. A. Al-Qassar, and A. S> M. Al-Obaidi, “Design a robust controller for congestion avoidance in TCP/AQM system,” Advances in Engineering Software, vol. 176, p. 103395, 2023, https://doi.org/10.1016/j.advengsoft.2022.103395.
R. Xu and M. Zhou. "Sliding mode control with sigmoid function for the motion tracking control of the piezo‐actuated stages," Electronics Letters, vol.53, no.2, pp.75-77, 2017, https://doi.org/10.1049/el.2016.3558.
L. H. Abood, B. K. Oleiwi, and E. H. Ali, “Optimal backstepping controller for controlling DC motor speed,” Bulletin of Electrical Engineering and Informatics, vol. 11, no. 5, pp. 2564-2572, 2022, https://doi.org/10.11591/eei.v11i5.3940.
S. A. Eissa, S. W. Shneen, and E. H. Ali, “Flower Pollination Algorithm to Tune PID Controller of TCP/AQM Wireless Networks,” Journal of Robotics and Control (JRC), vol. 4, no. 2, pp. 149-156, 2023, https://doi.org/10.1007/978-981-33-6104-1_10.
H. Yang, Y. Qi, and G. Du, “Image Matching Method Based on Laplacian Feature Constrained Coupling Variance Measure,” In IOP Conference Series: Materials Science and Engineering, vol. 750, no. 1, p. 012222, 2020, https://doi.org/10.1088/1757-899X/750/1/012222.
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