Journal of the Institute of Electronics and Information Engineers (전자공학회논문지)

The Institute of Electronics and Information Engineers (대한전자공학회)
권호 표지
DOI QR코드

DOI QR Code

Efficient Image Denoising Method Using Non-local Means Method in the Transform Domain

변환 영역에서 Non-local Means 방법을 이용한 효율적인 영상 잡음 제거 기법

  • Kim, Dong Min (School of Information, Communications and Electronics Engineering, Catholic University) ;
  • Lee, Chang Woo (School of Information, Communications and Electronics Engineering, Catholic University)
  • 김동민 (가톨릭대학교 정보통신전자공학부) ;
  • 이창우 (가톨릭대학교 정보통신전자공학부)
  • Received : 2016.07.18
  • Accepted : 2016.09.26
  • Published : 2016.10.25
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, an efficient image denoising method using non-local means (NL-means) method in the transform domain is proposed. Survey for various image denoising methods has been given, and the performances of the image denoising method using NL-means method have been analyzed. We propose an efficient implementation method for NL-means method by calculating the weights for NL-means method in the DCT and LiftLT transform domain. By using the proposed method, the computational complexity is reduced, and the image denoising performance improves by using the characteristics of images in the tranform domain efficiently. Moreover, the proposed method can be applied efficiently for performing image denoising and image rescaling simultaneously. Extensive computer simulations show that the proposed method shows superior performance to the conventional methods.

본 논문에서는 변환 영역에서 non-local means (NL-means) 방법을 이용한 효율적인 영상 잡음 제거 기법을 제안한다. 먼저 고전적인 영상 잡음 제거 기법에서부터 최근 연구되고 있는 영상 잡음 제거 기법에 대한 리뷰를 서술하고 우수한 성능을 보이는 잡음 제거 기법인 NL-means 방법을 이용한 영상 잡음 제거 기법에 대한 성능을 분석한다. NL-means 기법의 가중치를 DCT 및 LiftLT 변환 영역에서 일부 계수만을 이용하여 계산함으로써 NL-means 기법을 효율적으로 구현하는 방법을 제안한다. 제안하는 방법은 계산량을 줄여서 영상 잡음을 효율적으로 제거할 수 있을 뿐만 아니라 변환 영역에서 영상의 특성을 효율적으로 이용하여 잡음 제거시 성능을 향상시킨다. 또한 제안하는 기법은 변환 영역에서 영상의 잡음 제거와 해상도 향상을 동시에 수행할 때 효율적으로 적용할 수 있는 장점이 있다. 모의 실험을 통하여 제안하는 방법이 우수한 성능을 보이는 것을 입증한다.

Keywords

References

  1. M. C. Motwani, M. C. Gadiya, R. C. Motwani and F. C. Harris, "Survey of image denoising techniques," in Proc. of Global Signal Processing Expo and Conference (GSPx) 2004, Santa Clara, California, USA, Sept. 2004.
  2. J. Portilla, V. Strela, M. J. Wainwright and E.P. Simoncelli, "Image denoising using scale mixtures of gaussian in the wavelet domain," IEEE Trans. on Image Processing, Vol. 12, no. 11, pp. 1338-1351, Nov. 2003. https://doi.org/10.1109/TIP.2003.818640
  3. M. Zhang and B. K. Gunturk, "Multiresolution bilateral filtering for image denoising," IEEE Trans. on Image Processing, Vol. 17, no. 12, pp. 2324-2333, Dec. 2008. https://doi.org/10.1109/TIP.2008.2006658
  4. K. Dabov, A. Foi and V. Katkovnik and K. Egiazarian, "Image denoising by sparse 3-D transform domain collaborative filtering," IEEE Trans. on Image Processing, Vol. 16, no. 8, pp. 2080-2095, Aug. 2007. https://doi.org/10.1109/TIP.2007.901238
  5. A. Buades, B. Coll and J.-M. Morel, "A non-local algorithm for image denoising," in Proc. of Computer Vision and Pattern Recognition 2005 (CVPR 2005), pp. 60-65, June 2005.
  6. M. Mahmoudi and G. Sapiro, "Fast image and video denoising via nonlocal means of similar neighborhoods," IEEE Signal Processing Letters, Vol. 12, no. 12, pp. 839-842, Dec. 2005. https://doi.org/10.1109/LSP.2005.859509
  7. J. V. Manjon, P. Coupe, L. Martí-Bonmatí, D. L. Collins and M. Robles, "Adaptive non-local means denoising of MR images with spatially varying noise levels," Journal of Magnetic Resonance Imaging, Vol. 31, no. 1, pp. 192-203, Jan. 2010. https://doi.org/10.1002/jmri.22003
  8. D. M. Kim and C. W. Lee, "Efficient image denoising method using non-local means method," in Proc. of 2016 Image Processing and Image Understanding Workshop, Jeju, Korea, Feb. 2016.
  9. H. C. Burger and C. J. Schuler and S. Harmeling, "Image denoising: Can plain neural networks compete with BM3D?," in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2012, pp. 2392-2399, June 2012.
  10. J. Xie, L. Xu, and E. Chen, "Image denoising and inpainting with deep neural networks.," Advances Neural Inform. Process. Syst., Vol. 26, pp. 1-8, Feb. 2012.
  11. C. W. Lee, "General methods for L/M-fold resizing of compressed images using lapped transforms," IET Image Process., Vol. 1, no. 3, pp. 295-303, Sept. 2007. https://doi.org/10.1049/iet-ipr:20060088
  12. H. S. Hou, "A fast recursive algorithm for computing the discrete cosine transform," IEEE Trans. Acoust. Speech, Signal Process., Vol. 35, no. 10, pp. 1455-1461, Oct. 1987. https://doi.org/10.1109/TASSP.1987.1165060