Journal of IKEEE (전기전자학회논문지)

Institute of Korean Electrical and Electronics Engineers (한국전기전자학회)
권호 표지
DOI QR코드

DOI QR Code

Non-Local Means Denoising Method using Weighting Function based on Mixed norm

혼합 norm 기반의 가중치 함수를 이용한 평균 노이즈 제거 기법

  • Received : 2016.02.12
  • Accepted : 2016.06.22
  • Published : 2016.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

This paper presents a non-local means (NLM) denoising algorithm based on a new weighting function using a mixed norm. The fidelity of the difference between an anchor patch and the reference patch in the NLM denoising depends on noise level and local activity. This paper introduces a new weighting function based on a mixed norm type of which the order is determined by noise level and local activity of an anchor patch, so that the performance of the NLM denoising can be enhanced. Experimental results demonstrate the objective and subjective capability of the proposed algorithm. In addition, it was verified that the proposed algorithm can be used to improve the performance of the other $l_2$ norm based non-local means denoising algorithms

본 논문에서는 혼합 norm을 이용한 가중치 함수 기반의 비국부 평균 노이즈 제거 방식을 제안한다. 비국부 평균 노이즈 제거 방식에서 중심 패치와 참조 패치의 오차에 대한 신뢰도는 노이즈 양 및 국부 활동성에 의존적인 특성을 갖고 있다. 본 논문에서는 혼합 norm 기반의 새로운 가중치 함수를 제안하고, 혼합 norm의 차수를 노이즈 정도 및 중심 패치의 국부 활동성에 의해 적응적으로 결정하여 비국부 평균 노이즈 제거 방식의 성능을 개선하고자 하였다. 실험 결과를 통해 기존의 비국부 평균 노이즈 제거 방식과 비교하여 제안 방식의 정량적 및 정성적 성능의 우수성을 확인할 수 있었다. 더불어, 제안 방식은 표준 유클리드 norm 기반의 다른 형태의 비국부 평균 노이즈 방식의 성능을 개선할 수 있는 능력이 있음을 확인할 수 있었다.

Keywords

References

  1. P. Milanfar, "A tour of modern image filtering: New insights and methods, both practical and theoretical," IEEE Signal Processing Magazine, vol. 30, no. 1, pp. 106-128, Jan. 2013. https://doi.org/10.1109/MSP.2011.2179329
  2. A. Buades, B. Coll, and J. M. Morel, "A non-local algorithm for image denoising," IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 60-65, June 2005.
  3. T. A. Thacker, J. Manjon, and P. A. Bromiley, "Statistical interpretation of non-local means," IET Computer Vision, vol. 4, no. 3, pp. 162-172, Mar. 2010. https://doi.org/10.1049/iet-cvi.2008.0076
  4. V. Duval, J. F. Aujol, and Y. Gousseau, "A bias-variance approach for the nonlocal means," SIAM J. Imaging Sciences, vol. 4, no. 2, pp. 760-788, May 2011. https://doi.org/10.1137/100790902
  5. Y. Wu, B. Tracey, P. Natranjan, and J. Noonan, "James-Stein type center pixel weights for non-local means image denoising," IEEE Signal Processing Letters, vol. 20, no. 4, pp. 411-414, Apr. 2013. https://doi.org/10.1109/LSP.2013.2247755
  6. W. Zeng, X. Lu, and S. Fei, "NLM denoising method with adaptive center pixel weights," The Seventh Int. Symp. Computational Intelligence and Design, pp. 166-169, Dec. 2014.
  7. C. A. Deledalle, V. Duval, and J. Salmon, "Non-local methods with shape adaptive patches," J. of Mathematical Imaging and Vision, vol. 43, no. 2, pp. 103-120, June 2012. https://doi.org/10.1007/s10851-011-0294-y
  8. W. L. Zeng and X. B. Lu, "Region-based non-local means algorithm for noise removal," Electronics Letters, vol. 47, no. 20, pp. 1125-1127, Sept. 2011. https://doi.org/10.1049/el.2011.2456
  9. D. H. P. Salvadeo, N. D. A. Mascarenhas, A. L. M. Levada, "Nonlocal markovian model for image denoising," J. of Electronic Imaging, vol. 25, no. 1, doi:10.1117/1.JEI.25.1.013003, Jan. 2016.
  10. K. Chaudhury, "Acceleration of the shiftable O(1) algorithm for bilateral filtering and non-local means," IEEE Trans. Image Processing, vol. 22, no. 4, pp. 1291-1300, Apr. 2013. https://doi.org/10.1109/TIP.2012.2222903
  11. E. Walach and B. Widrow, "The least mean fourth (lmf) adaptive algorithm and its familiy," IEEE Trans. Information Theory, vol. IT-30, no. 2, pp. 275-283, March 1984.
  12. Z. Wang and A. C. Bovik, "Image Quality Assessment: From Error Visibility to Structural Similarity," IEEE Trans. Image Processing, vol. 13, no. 4, April 2004