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

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Image Denoising Using Nonlocal Similarity and 3D Filtering

비지역적 유사성 및 3차원 필터링 기반 영상 잡음제거

  • Kim, Seehyun (Department of Information and Communications Engineering, The University of Suwon)
  • Received : 2017.08.24
  • Accepted : 2017.09.04
  • Published : 2017.10.31
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Abstract

Denoising which is one of major research topics in the image processing deals with recovering the noisy images. Natural images are well known not only for their local but also nonlocal similarity. Patterns of unique edges and texture which are crucial for understanding the image are repeated over the nonlocal region. In this paper, a nonlocal similarity based denoising algorithm is proposed. First for every blocks of the noisy image, nonlocal similar blocks are gathered to construct a overcomplete data set which are inherently sparse in the transform domain due to the characteristics of the images. Then, the sparse transform coefficients are filtered to suppress the non-sparse additive noise. Finally, the image is recovered by aggregating the overcomplete estimates of each pixel. Performance experiments with several images show that the proposed algorithm outperforms the conventional methods in removing the additive Gaussian noise effectively while preserving the image details.

영상 신호 처리 분야 중 잡음제거(denoising)는 가산 잡음이 포함된 영상의 복원을 다룬다. 자연 영상은 지역적 유사성 뿐 만 아니라 비지역적 유사성도 높다는 점은 널리 알려져 있다. 즉, 입력 영상의 특성을 결정짓는 에지나 텍스쳐 패턴이 떨어져 있는 영역에서도 반복적으로 나타난다. 본 논문에서는 비지역적 유사도가 높은 영상 블록을 검출하여 과충분한 신호 집합을 만들고 이를 3차원 변환을 통해 희소(sparse)하게 표현한 후 영상에 포함된 잡음 성분을 제거하는 잡음제거 알고리듬을 제안한다. 여러 영상에 대해 잡음제거 결과로부터 제안된 알고리듬이 부드러운 영역과 에지 영역을 잘 살려 원 영상을 복원할 수 있음을 알 수 있고, 또한 최근 보고된 여러 잡음제거 알고리듬들과의 복원 성능 비교를 통해 상대적으로 우수한 성능을 보임을 확인하였다.

Keywords

References

  1. R. Gonzalez and R. Woods, Digital Image Processing, 3rd Ed., Pearson, New Jersey 2010.
  2. S. Kim, "A compressive sensing based imaging algorithm using incoherent measurements and DCT," Journal of the Korea Institute of Information and Communication Engineering, vol. 20, no. 10, pp. 1961-1966, Oct. 2016. https://doi.org/10.6109/jkiice.2016.20.10.1961
  3. L. Condat, "A direct algorithm for 1D total variation denoising," IEEE Signal Processing Letters, vol. 20, no. 11, pp. 1054-1057, Nov. 2013. https://doi.org/10.1109/LSP.2013.2278339
  4. B.-W. Hong, et. al., "A new model and simple algorithms for Multi-label Mumford-Shah problems," Proc. EEE Conference on Computer Vision and Pattern Recognition, 2013, pp. 1219-1226.
  5. A. Buades, et. al, "A non-local algorithm for image denoising," Proc. International Conference on Computer Vision Pattern Recognition, pp. 60-69, 2005.
  6. G. Gilboa and S. Osher, "Nonlocal operators with applications to image processing," SIAM Multiscale Modeling & Simulation., vol. 7, no. 3, pp. 1005-1028, 2008.
  7. M. Jung, et. al, "Nonlocal Mumford-Shah regularizers for color image restoration," IEEE Trans., Image Processing, vol. 20, no. 6, pp. 1583-1598, Jun. 2011. https://doi.org/10.1109/TIP.2010.2092433
  8. S. Shan, et. al., "BM3D denoising based on minimum GCV score," Proc. International Conference on Computers, Communications, and Systems, pp. 154-158, 2015.
  9. M. Nilchian, et. al, "Optimized Kaiser-Bessel window functions for CT," IEEE Trans., Image Processing, vol. 24, no. 11, pp. 3826-3833, Dec. 2015. https://doi.org/10.1109/TIP.2015.2451955
  10. J. Zhang, et. al., "Image restoration using joint statistical modeling in a space-transform domain," IEEE Trnas., Circuits and Systems for Video Technology, vol. 24, no. 6, pp. 915-928, June 2014. https://doi.org/10.1109/TCSVT.2014.2302380
  11. J. A. Guerrero-Colon and J. Portilla, "Two-level adaptive denoising using Gaussian scale mixtures in overcomplete oriented pyramids," Proc. IEEE International Conference on Image Processing, pp. 105-108, Sep. 2005.