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Image Restoration Based on Inverse Filtering Order and Power Spectrum Density

역 필터 순서와 파워 스펙트럼 밀도에 기초한 이미지 복원

  • 김용길 (조선이공대학교 컴퓨터보안과) ;
  • 문경일 (호남대학교 공과대학 컴퓨터공학과)
  • Received : 2016.02.23
  • Accepted : 2016.04.08
  • Published : 2016.04.30

Abstract

In this paper, we suggest a approach which comprises fast Fourier transform inversion by wavelet noise attenuation. It represents an inverse filtering by adopting a factor into the Wiener filtering, and the optimal factor is chosen to minimize the overall mean squared error. in order to apply the Wiener filter, we have to compute the power spectrum of original image from the corrupted figure. Since the Wiener filtering contains the inverse filtering process, it expands the noise when the blurring filter is not invertible. To remove the large noises, the best is to remove the noise using wavelet threshold. Wavelet noise attenuation steps are consisted of inverse filtering and noise reduction by Wavelet functions. experimental results have not outperformed the other methods over the overall restoration performance.

본 연구에서는, 웨이블릿 노이즈 감쇠에 고속 푸리에 역 변환을 포함하는 방법을 제안한다. 위너 필터링에 인자를 채용하여 역 필터링을 나타내고, 최적의 계수는 전체 평균 제곱 오차를 최소화하도록 선택된다. 위너 필터를 적용하기 위해, 손상된 그림에서 원 화상의 파워 스펙트럼을 계산한다. 위너 필터링은 역 필터링 처리를 포함하기 때문에 블링 필터가 반전되지 않을 때 노이즈는 확장한다. 큰 노이즈를 제거하려면 최고의 웨이블릿 임계값을 사용하여 노이즈를 제거하는 것이다. 웨이블릿 노이즈 감쇠 단계는 역 필터링 및 웨이블릿 기능으로 노이즈 감소로 구성된다. 실험결과는 전체 재생 성능 이상의 다른 방법을 능가하지는 않았다.

Keywords

References

  1. Buades, A., B. Coll, et al, "A review of image denoising algorithms, with a new one," Multiscale Modeling and Simulation, Vol.4, No.2, pp.490-530, 2006. https://doi.org/10.1137/040616024
  2. Chen, G. and T. Bui , "Multi wavelets denoising using neighboring coefficients," Signal Processing Letters, IEEE Vol.10, No.7, pp.211-214, 2003. https://doi.org/10.1109/LSP.2003.811586
  3. Portilla, J., V. Strela, et al, "Image denoising using scale mixtures of Gaussians in the wavelet domain," Image Processing, IEEE Transactions on Vol.12, No.11, pp.1338-1351, 2003. https://doi.org/10.1109/TIP.2003.818640
  4. Do, M. and M. Vetterli, "Texture similarity measurement using Kullback-Leibler distance on wavelet subbands," IEEE International Conference on Image Processing (ICIP2000), Vol. 3, pp.1-4, 2000.
  5. Heric, D. and D. Zazula, "Combined edge detection using Wavelet transform and signal registration," Image and Vision Computing, Vol.25, No.5, pp.652-662, 2007. https://doi.org/10.1016/j.imavis.2006.05.008
  6. Meyer, F., and A. Averbuch, "Fast adaptive wavelet packet image compression," Image Processing, IEEE Transactions on Vol.9, No.5, pp.792-800, 2002. https://doi.org/10.1109/83.841526
  7. Innho Jee, "A study on the Performance Improvement of Over-sampled discrete Wavelet Transform," The Journal of The Institute of Internet, Broadcasting and Communication (JIIBC),Vol.14,No.1, pp. 69-76, 2014 https://doi.org/10.7236/JIIBC.2014.14.1.69
  8. Ergen, B., Y. Tatar , "Time-frequency analysis of phonocardiogram signals using wavelet transform: a comparative study," Computer Methods in Biomechanics and Biomedical Engineering, Vol.9999, No.1, pp.1-1, 2010.
  9. Rubeena, V., and T. Akash, "Image restoration using thresholding Techniques on Wavelet Coefficients," International Journal of Computer Science Issues(IJCSI), Vol. 8, No.3, pp.400-404, 2011.
  10. Monika, S., and C. Soni, "A Comparative study of Wavelet and Curvelet Transform for Image Denoising," IOSR Journal of Electronics and Communication Engineering, Vol.7, No.4, pp.63-68, 2013. https://doi.org/10.9790/2834-0746368
  11. Ufade, A. S., B. K. Khadse, and S. R. Suralkar, "Restoration of blur image using wavelet based image fusion," International Journal of Engineering and Advanced Technology (IJEAT), Vol.2, No.2, pp.159-161, 2012.