DOI QR코드

DOI QR Code

고밀도 임펄스 잡음에 훼손된 영상 복원을 위한 적응형 가중치 필터 알고리즘

Adaptive Weight Filter Algorithm for Restoration Images Corrupted by High Density Impulse Noise

  • Cheon, Bong-Won (Department of Intelligent Robot Eng., Pukyong National University) ;
  • Kim, Nam-Ho (School of Electrical Engineering, Pukyong National University)
  • 투고 : 2022.08.10
  • 심사 : 2022.08.30
  • 발행 : 2022.10.31

초록

최근 4차 산업혁명의 영향과 통신매체의 발전으로 다양한 디지털 영상장비가 산업현장에서 사용되고 있다. 영상 데이터는 카메라와 센서로부터 취득되는 과정 및 송수신 과정에서 잡음에 훼손되기 쉬우며, 훼손된 영상은 시스템의 처리과정에 영향을 미치기 때문에 잡음제거가 필수적으로 선행되고 있다. 본 논문에서는 고밀도의 임펄스 잡음에 훼손된 영상을 복원하기 위해 가중치 그래프를 사용한 가중치 필터 알고리즘을 제안하였다. 제안한 알고리즘은 영상의 필터링 마스크 내부의 화소값을 사용하여 가중치 그래프를 구하였으며, 최종 가중치를 필터링 마스크에 적용하여 영상을 복원하였다. 제안하는 알고리즘의 잡음제거 성능을 분석하기 위해 시뮬레이션을 진행하였으며, 확대영상 및 PSNR을 사용하여 기존 방법과 비교하였다. 제안한 알고리즘의 결과 영상은 고밀도 임펄스 잡음을 제거하며 우수한 성능을 보였다.

Recently, due to the influence of the 4th industrial revolution and the development of communication media, various digital video equipment are being used in industrial fields. Image data is easily damaged by noise in the process of acquiring and transmitting and receiving from the camera and sensor, and since the damaged image has a great effect on the processing of the system, noise removal is essential. In this paper, a weight filter algorithm using a weight graph is proposed to restoration images damaged by high-density impulse noise. The proposed algorithm obtains a weight graph using pixel values inside the filtering mask of the image, and restores the image by applying the final weight to the filtering mask. Simulation was conducted to analyze the noise removal performance of the proposed algorithm, and the magnified image and PSNR were used to compare with the existing method. The resulting image of the proposed algorithm showed excellent performance by removing high-density impulse noise.

키워드

참고문헌

  1. D. Chowdhury, S. K. Das, S. Nandy, A. Chakraborty, R. Goswami, and A. Chakraborty, "An Atomic Technique for Removal of Gaussian Noise From a Noisy Gray Scale Image using Low Pass-Convoluted Gaussian Filter," in Proceedings of 2019 International Conference on Opto-Electronics and Applied Optics (Optronix), Kolkata, India, pp. 1-6, 2019.
  2. P. S. V. S. Sridhar and R. Caytiles, "Efficient Cloud Data Hosting Availability," Asia-pacific Journal of Convergent Research Interchange, vol. 3, no. 2, pp. 11-19, Jun. 2017. https://doi.org/10.21742/APJCRI.2017.06.02
  3. B. W. Cheon and N. H. Kim, "A Filter Algorithm based on Partial Mask and Lagrange Interpolation for Impulse Noise Removal," Journal of the Korea Institute of Information and Communication Engineering, vol. 26, no. 5, pp. 675-681, May 2022. https://doi.org/10.6109/JKIICE.2022.26.5.675
  4. W. S. Lee and Y. S. Choi, "Impulse Noise Immune Bayer Image Compression with Direction Estimation for Imaging Sensor," in Proceedings of 2019 26th IEEE International Conference on Electronics, Circuits and Systems, Genoa, Italy, pp. 670-673, 2019.
  5. C. Lin, Y. Li, S. Feng, and M. Huang, "A Two-Stage Algorithm for the Detection and Removal of RandomValued Impulse Noise based on Local Similarity," IEEE Access, vol. 8, no. 1, pp. 222001-222012, Nov. 2020. https://doi.org/10.1109/ACCESS.2020.3040760
  6. M. Mafi, H. Rajaei, M. Cabrerizo, and M. Adjouadi, "A Robust Edge Detection Approach in the Presence of High Impulse Noise Intensity Through Switching Adaptive Median and Fixed Weighted Mean Filtering," IEEE Transactions on Image Processing, vol. 27, no. 11, pp. 5475-5490, Nov. 2018. https://doi.org/10.1109/TIP.2018.2857448
  7. N. T. Trung, D. H. Trinh, N. L. Trung, T. T. T. Quynh, and M. H. Luu, "Dilated Residual Convolutional Neural Networks for Low-Dose CT Image Denoising," in Proceedings of 2020 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS), Ha Long, Vietnam, pp. 189-192, 2020.
  8. P. H. T. Binh, C. Cruz, and K. Egiazarian, "Flashlight CNN Image Denoising," in Proceedings of 2020 28th European Signal Processing Conference (EUSIPCO), Amsterdam, Netherlands, pp. 670-674, 2021.
  9. M. M. Hamid, F. F. Hammad, and N. Hmad, "Removing the Impulse Noise from Grayscaled and Colored Digital Images using Fuzzy Image Filtering," in Proceedings of 2021 IEEE 1st International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering MI-STA, Tripoli, Libya, pp. 711-716, 2021.
  10. X. Xiao, N. N. Xiong, J. Lai, C. D. Wang, Z. Sun, and J. Yan, "A Local Consensus Index Scheme for Random-Valued Impulse Noise Detection Systems," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 6, pp. 3412-3428, Jun. 2021. https://doi.org/10.1109/TSMC.2019.2925886
  11. Z. He, J. Zhang, and H. Zhang, "A Compressed Sensing Denoising Algorithm for Astronomical Images," in Proceedings of 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chengdu, China, pp. 596-600, 2020.
  12. J. S. Lee, S. J. Ko, S. S. Kang, J. H. Kim, D. H. Kim, and C. S. Kim, "Quantitative Evaluation of Image Quality using Automatic Exposure Control & Sensitivity in the Digital Chest Image," The Journal of the Korea Contents Association, vol. 13, no. 8, pp. 275-283, Aug. 2013. https://doi.org/10.5392/JKCA.2013.13.08.275