한국정보통신학회논문지 (Journal of the Korea Institute of Information and Communication Engineering)

  • 제24권12호
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  • Pages.1625-1631
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  • 2020
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  • 2234-4772(pISSN)
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  • 2288-4165(eISSN)
한국정보통신학회 (The Korea Institute of Information and Commucation Engineering)
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AWGN 환경에서 퍼지 멤버십 함수에 기반한 잡음 제거 알고리즘

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)
  • 투고 : 2020.09.11
  • 심사 : 2020.11.06
  • 발행 : 2020.12.31
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⟨ 이전 논문 다음 논문 ⟩

초록

IoT 기술의 발달에 따라 다양한 디지털 장비가 보급되고 있으며, 이에 따라 데이터 처리의 중요성이 높아지고 있다. 데이터 처리는 장비의 신뢰성에 큰 영향을 미치는 만큼 그 중요성이 증가하고 있으며, 다양한 연구가 진행되고 있다. 본 논문에서는 퍼지 멤버쉽 함수의 특성에 따른 AWGN을 제거하는 알고리즘을 제안한다. 제안한 알고리즘은 입력 영상 및 필터링 마스크 내부의 화소값 사이의 퍼지 멤버쉽 함수값의 상관관계에 따라 추정치를 계산하며, 공간 가중치 필터의 출력과 가감하여 최종 출력을 구한다. 제안한 알고리즘을 평가하기 위해 기존 AWGN 제거 알고리즘들과 시뮬레이션하였으며, 차영상 및 PSNR 비교를 사용하여 분석하였다. 제안한 알고리즘은 잡음의 영향을 최소화하였으며, 영상의 중요 특성을 보존하며 효율적으로 잡음을 제거하는 성능을 보였다.

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.

키워드

과제정보

This work was supported by the Technology development Program(S2922914) funded by the Ministry of SMEs and Startups(MSS, Korea)

참고문헌

  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. https://doi.org/10.9717/KMMS.2019.22.1.010
  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.
  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. https://doi.org/10.1016/j.nucengdes.2018.05.026
  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. https://doi.org/10.1109/TMI.2015.2475356
  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. https://doi.org/10.1109/TFUZZ.2015.2446508
  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. https://doi.org/10.1109/TFUZZ.2019.2911494
  16. 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. https://doi.org/10.6109/jkiice.2015.19.6.1296
  17. 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.