DOI QR코드

DOI QR Code

Effective Noise Reduction using STFT-based Content Analysis

STFT 기반 영상분석을 이용한 효과적인 잡음제거 알고리즘

  • Baek, Seungin (Department of Image Engineering, Graduate School of Advanced Imaging Science, Multimedia, and Film, Chung-Ang University) ;
  • Jeong, Soowoong (Department of Image Engineering, Graduate School of Advanced Imaging Science, Multimedia, and Film, Chung-Ang University) ;
  • Choi, Jong-Soo (Department of Image Engineering, Graduate School of Advanced Imaging Science, Multimedia, and Film, Chung-Ang University) ;
  • Lee, Sangkeun (Department of Image Engineering, Graduate School of Advanced Imaging Science, Multimedia, and Film, Chung-Ang University)
  • 백승인 (중앙대학교 첨단영상대학원 영상학과) ;
  • 정수웅 (중앙대학교 첨단영상대학원 영상학과) ;
  • 최종수 (중앙대학교 첨단영상대학원 영상학과) ;
  • 이상근 (중앙대학교 첨단영상대학원 영상학과)
  • Received : 2014.11.14
  • Accepted : 2015.04.01
  • Published : 2015.04.25

Abstract

Noise reduction has been actively studied in the digital image processing and recently, block-based denoising algorithms are widely used. In particular, a low rank approximation employing WNNM(Weighted Nuclear Norm Minimization) and block-based approaches demonstrated the potential for effective noise reduction. However, the algorithm based on low rank a approximation generates the artifacts in the image restoration step. In this paper, we analyzes the image content using the STFT(Short Time Fourier Transform) and proposes an effective method of minimizing the artifacts generated from the conventional algorithm. To evaluate the performance of the proposed scheme, we use the test images containing a wide range of noise levels and compare the results with the state-of-art algorithms.

디지털 영상 처리 분야에서 잡음 제거는 활발히 연구되어오고 있으며, 최근에는 블록 기반의 잡음 제거 알고리즘이 널리 사용되고 있다. 저계수행렬 근사 기반의 잡음 제거 알고리즘은 WNNM(Weighted Nuclear Norm Minimization)과 블록 기반의 잡음 제거 방법을 적용하여 잡음 제거 방법에 대한 잠재력을 입증했다. 그러나 저계수행렬 근사 기반의 잡음 제거 알고리즘은 영상복원 과정에서 의도치 않은 아티팩트를 발생시킨다. 본 논문에서는 STFT(Short Time Fourier Transform)을 이용해 영상을 분석하여 기존 알고리즘에서 발생하는 아티팩트를 적응적으로 최소화시키는 방법을 제안한다. 성능을 확인하기 위해 다양한 잡음정도를 포함하는 영상에서 실험하였으며, 비교를 통해 제안된 방법이 기존의 잡음 제거 알고리즘보다 효과적으로 잡음을 제거하는 것을 확인했다.

Keywords

References

  1. R. C. Gonzalez and R. E. Woods, Digital Image Processing Third Edition, Pearson Prentice-Hall, 2008.
  2. Sung-Un Kim, "An Image Denoising Algorithm for the Mobile Phone Cameras," The Journal of the Korea Institute of Electronic Communication Sciences, vol. 9, no. 5, pp. 601-607, May 2014. https://doi.org/10.13067/JKIECS.201.9.5.601
  3. C. Tomasi and R. Manduchi, "Bilateral filtering for gray and color images," in Proceedings of IEEE International Conference on Computer Vision, pp. 839-846, Bombay, India, Jan. 1998.
  4. A. Buades, B. Coll, and J.M. Morel "A non-local algorithm for image denoising," in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 60-65, San Diego, CA, USA, June 2005.
  5. Sang Wook Park and Moon Gi Kang, "Improved Nonlocal Means Algorithm for Image Denoising," Journal of the Institute of Electronics Engineers of Korea, vol. 48. no. 1, pp. 46-53, Jan. 2011.
  6. Sang Wook Park and Moon Gi Kang, "Spatio-temporal Denoising Algorithm base on Nonlocal Means," Journal of the Institute of Electronics Engineers of Korea, vol. 48, no. 2, pp. 24-31, Mar. 2011.
  7. A. Foi, V. Katkovnik, and K. Egiazarian, "Pointwise Shape-Adaptive DCT for High-Quality Denoising and Deblocking of Grayscale and Color Images," IEEE Transactions on Image Processing, vol. 16, no. 5, pp. 1395-1411, May 2007. https://doi.org/10.1109/TIP.2007.891788
  8. K. Dabov, A. Foi, V. Katkovnik, and K.Egiazarian, "Image denoising by sparse 3d transform domain collaborative filtering," IEEE Transactions on Image Processing, vol. 16, no. 8, pp. 2080-2095, Aug. 2007. https://doi.org/10.1109/TIP.2007.901238
  9. Claude Knaus and Matthias Zwicker, "DUAL-DOMAIN IMAGE DENOISING," in Proceedings of the International Conference on Image Processing, pp. 440-444, Melbourne, VIC, Australia, Sep. 2013.
  10. Claude Knaus and Matthias Zwicker, "Progressive Image Denoising," IEEE Transactions on Image Processing, vol. 23, no. 7, pp. 3114-3125, July 2014. https://doi.org/10.1109/TIP.2014.2326771
  11. M. Elad and M. Aharon, "Image denoising via sparse and redundant representations over learned dictionaries," IEEE Transactions on Image Processing, vol. 15, no. 12, pp. 3736-3745, Dec. 2006. https://doi.org/10.1109/TIP.2006.881969
  12. S. Gu, L. Zhang, W. Zuo, and X. Feng, "Weighted Nuclear Norm Minimization with Application to Image Denoising," in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2862-2869, Columbus, OH, USA, June 2014.
  13. J. Cai, E. Candes, and Z. Shen, "A singular value thresholding algorithm for matrix completion," SIAM Journal on Optimization, vol. 20, no. 4, pp. 1956-1982, Jan. 2010. https://doi.org/10.1137/080738970
  14. J. B. Allen, "Short term spectral analysis, synthesis, and modification by discrete fourier transform," in Proceedings of IEEE Conference on Acoustics, Speech and Signal Processing, vol. 25, no. 3, pp. 235-238, Jun. 1977.