Journal of the Institute of Convergence Signal Processing (융합신호처리학회논문지)

The Korea Institute of Convergence Signal Processing (한국융합신호처리학회)
권호 표지
DOI QR코드

DOI QR Code

Weighted Filter Algorithm based on Distribution Pattern of Pixel Value for AWGN Removal

AWGN 제거를 위한 화소값 분포패턴에 기반한 가중치 필터 알고리즘

  • Cheon, Bong-Won (Dept. of Intelligent Robot Eng., Pukyong National University) ;
  • Kim, Nam-Ho (School of Electrical Eng., Pukyong National University)
  • 천봉원 (부경대학교 지능로봇공학과) ;
  • 김남호 (부경대학교 공과대학 전기공학부)
  • Received : 2022.03.16
  • Accepted : 2022.03.24
  • Published : 2022.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

Abstract Recently, with the development of IoT technology and communication media, various video equipment is being used in industrial fields. Image data acquired from cameras and sensors are easily affected by noise during transmission and reception, and noise removal is essential as it greatly affects system reliability. In this paper, we propose a weight filter algorithm based on the pixel value distribution pattern to preserve details in the process of restoring images damaged in AWGN. The proposed algorithm calculates weights according to the pixel value distribution pattern of the image and restores the image by applying a filtering mask. In order to analyze the noise removal performance of the proposed algorithm, it was simulated using enlarged image and PSNR compared to the existing method. The proposed algorithm preserves important characteristics of the image and shows the performance of efficiently removing noise compared to the existing method.

최근 IoT 기술과 통신매체의 발전에 따라 다양한 영상 장비가 산업 현장에서 사용되고 있다. 카메라와 센서에서 취득된 영상 데이터는 송수신 과정에서 잡음의 영향을 받기 쉬우며, 시스템의 신뢰성에 큰 영향을 미치는 만큼 잡음 제거가 필수적으로 선행되고 있다. 본 논문에서는 AWGN에 훼손된 영상을 복원하는 과정에서 디테일을 보존하기 위해 화소값 분포패턴에 기반한 가중치 필터 알고리즘을 제안하였다. 제안한 알고리즘은 영상의 화소값 분포패턴에 따라 가중치를 계산하였으며, 필터링 마스크에 적용하여 영상을 복원하였다. 제안하는 알고리즘의 잡음 제거 성능을 분석하기 위해 기존 방법과 비교하여 확대영상 및 PSNR을 사용하여 시뮬레이션하였다. 제안한 알고리즘은 영상의 중요 특성을 보존하며 기존 방법에 비해 효율적으로 잡음을 제거하는 성능을 보였다.

Keywords

References

  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 2019 International Conference on Opto-Electronics and Applied Optics (Optronix), Kolkata : India, pp. 1-6, 2019.
  2. P. S. V. S. Sridhar, 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. K. Kai, L. Tingting, X. Xianchun, Z. Guoquan, and Z. Jianxin, "Study of Infrared Image Denoising Algorithm based on Steering Kernel Regression Image Guided Filter," in 2019 18th International Conference on Optical Communications and Networks (ICOCN), Huangshan : China, pp. 1-3, 2019.
  4. B. W. Cheon, N. H. Kim, "Modified Gaussian Filter Algorithm using Quadtree Segmentation in AWGN Environment," Journal of the Korea Institute of Information and Communication Engineering, vol. 25, no. 9, pp. 1176-1182, Sept. 2021. DOI: 10.6109/jkiice.2021.25.9.1176.
  5. X. Long, and N. H. Kim, "A Study on the Spatial Weighted Filter in AWGN Environment," Journal of the Korea Institute of Information and Communication Engineering, vol. 17, no. 3, pp. 724-729, Mar. 2013. https://doi.org/10.6109/JKIICE.2013.17.3.724
  6. D. H. Shin, R. H. Park, S. J. Yang, and J. H. Jung, "Block-based Noise Estimation using Adaptive Gaussian Filtering," in 2005 Digest of Technical Papers. International Conference on Consumer Electronics, Las Vegas : USA, 2005, pp. 263-264.
  7. S. I. Kwon, and N. H. Kim, "A Study on Composite Filter using Edge Information of Local Mask in AWGN Environments," Journal of the Korea Institute of Convergence Signal Processing, vol. 17, no. 2, pp. 71~76, Dec. 2016. https://doi.org/10.23087/JKICSP.2016.17.2.004