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http://dx.doi.org/10.6109/jkiice.2020.24.12.1625

Noise Removal Algorithm based on Fuzzy Membership Function in AWGN Environments  

Cheon, Bong-Won (Dept. of Smart Robot Convergence and Application Eng., Pukyong National University)
Kim, Nam-Ho (Dept. of Control and Instrumentation Eng., Pukyong National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.12, 2020 , pp. 1625-1631 More about this Journal
Abstract
With the development of IoT technology, various digital equipment is being spread, and accordingly, the importance of data processing is increasing. The importance of data processing is increasing as it greatly affects the reliability of equipment, and various studies are being conducted. In this paper, we propose an algorithm to remove AWGN according to the characteristics of the fuzzy membership function. The proposed algorithm calculates the estimated value according to the correlation between the value of the fuzzy membership function between the input image and the pixel value inside the filtering mask, and obtains the final output by adding or subtracting the output of the spatial weight filter. In order to evaluate the proposed algorithm, it was simulated with existing AWGN removal algorithms, and analyzed using difference image and PSNR comparison. The proposed algorithm minimizes the effect of noise, preserves the important characteristics of the image, and shows the performance of efficiently removing noise.
Keywords
Image processing; Fuzzy membership function; Noise removal; AWGN;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
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1 T. K. Kim, I. H. Song, and S. H. Lee, "Noise Reduction of HDR Detail Layer using a Kalman Filter Adapted to Local Image Activity," Journal of Korea Multimedia Society, vol. 22, no. 1, pp. 10-17, Jan. 2019.   DOI
2 P. S. V. S. Sridhar and R. Caytiles, "Efficient Cloud Data Hosting Availability," Asia-pacific Journal of Convergent Research Interchange, HSST, ISSN : 2508-9080, vol. 3, no. 2, pp. 11-19, Jun. 2017. http://dx.doi.org/10.21742/APJCRI.2017.06.02.   DOI
3 A. Rubel, O. Rubel, V. Abramova, G. Proskura, and V. Lukin, "Improved Noisy Image Quality Assessment using Multilayer Neural Networks," in 2019 IEEE 2nd Ukraine Conference on Electrical and Computer Engineering (UKRCON), Lviv : Ukraine, pp. 1046-1051, 2019.
4 G. Thanakumar, S. Murugappriya, and G. R. Suresh, "High Density Impulse Noise Removal using BDND Filtering Algorithm," in 2014 International Conference on Communication and Signal Processing, Melmaruvathur : India, pp. 1958-1962, 2014.
5 K. Chithra and T. Santhanam, "Hybrid Denoising Technique for Suppressing Gaussian Noise in Medical Images," in 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), Chennai : India, pp. 1460-1463, 2017.
6 S. Y. Kim, S. H. Yu, and J. C. Jeong, "A Wiener Filter Using Edge Detection for Gaussian Noise Reduction," in Conference on The Institute of Electronics and Information Engineers, Incheon : Korea, pp. 430-433, 2018.
7 M. Chowdhury, J. Gao, and R. Islam, "Fuzzy Logic Based Filtering for Image De-Noising," in 2016 IEEE International Conference on Fuzzy Systems, Vancouver, BC : Canada, pp. 2372-2376, 2016.
8 S. I. Jabbar, C. R. Day, and E. K. Chadwick, "Using Fuzzy Inference system for Detection the Edges of Musculoskeletal Ultrasound Images," in 2019 IEEE International Conference on Fuzzy Systems, New Orleans, LA : USA, pp. 1-7, 2019.
9 R. C. Buenoa, P. H. F. Masottib, J. F. Justoc, D. A. Andradeb, M. S. Rochab, W. M. Torresb, and R. N. de Mesquitab, "Two-phaseflow Bubble Detection Method Applied to Natural Circulationsystem using Fuzzy Image Processing," Journal of the Nuclear Engineering and Design, vol. 335, no. 15, pp. 255-264, Aug. 2018.   DOI
10 L. M. Herrera, M. I. C. Murguia, D. A. P. Urrutia, and J. A. R. Quintana, "Human Image Complexity Analysis using a Fuzzy Inference System," in 2019 IEEE International Conference on Fuzzy Systems, New Orleans, LA : USA, pp. 1-6, 2019.
11 P. Mohajerani and V. Ntziachristos, "An Inversion Scheme for Hybrid Fluorescence Molecular Tomography using a Fuzzy Inference System," Journal of the IEEE Transactions on Medical Imaging, vol. 35, no. 12, pp. 381-390, Feb. 2016.   DOI
12 J. M. Mendel, H. Hagras, H. Bustince, and F. Herrera, "Comments on Interval Type-2 Fuzzy Sets are Generalization of Interval-Valued Fuzzy Sets: Towards a Wide View on Their Relationship", Journal of the IEEE Transactions on Fuzzy Systems, vol. 24, no. 1, pp. 249-250, \Feb. 2016.   DOI
13 N. L. S. B. Albashah, S. C. Dass, V. S. Asirvadam, and F. Meriaudeau, "Segmentation Of Blood Clot MRI Images using Intuitionistic Fuzzy Set Theory," in 2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), Sarawak : Malaysia, pp. 533-538, 2018.
14 A. D. Belsare, M. M. Mushrif, and M. A. Pangarkar, "Breast Epithelial Duct Region Segmentation Using Intuitionistic Fuzzy Based Multi-Texture Image Map," in 2017 14th IEEE India Council International Conference (INDICON), Roorkee : India, pp. 1-6, 2017.
15 P. Hurtik, V. Molek, and Jan Hula, "Data Preprocessing Technique for Neural Networks Based on Image Represented by a Fuzzy Function," Journal of the IEEE Transactions on Fuzzy Systems, vol. 28, no. 7, pp. 1195-1204, Jul. 2020.   DOI
16 B. W. Cheon and N. H. Kim, "Noise Removal Algorithm Considering High Frequency Components in AWGN Environments," Journal of the Korea Institute of Information and Communication Engineerin, vol. 22, no. 6, pp. 867-873, Jun. 2018.
17 K. B. Kim, "Extracting Ganglion Cysts from Ultrasound Image with Fuzzy Membership Function," Journal of the Korea Institute of Information and Communication Engineerin, vol. 19, no. 6, pp. 1296-1300, Jun. 2015.   DOI