Multiple Decision Model for Image Denoising in Wavelet Transform Domain

웨이블릿 변환 영역에서 영상 잡음 제거를 위한 다중 결정 모델

  • Published : 2004.07.01

Abstract

A binary decision model which is used to denoising has demerits to measure the precise ratio of signal to noise because of only a binary classification. To supplement these demerits, complex statistical model and undecimated wavelet transform are generally exploited. In this paper, we propose a noise reduction method using a multi-level decision model for measuring the ratio of noise in noisy image. The propose method achieves good denoising performance with orthogonal wavelet transform because the ratio of signal to noise can be calculated to multi-valued form. In simulation results, the proposed denoising method outperforms 0.1dB in the PSNR sense than the state of art denoising algorithms using orthogonal wavelet transform.

잡음 제거에 사용되는 이진 결정 모델은 단지 이분적인 구분만을 수행하기 때문에 잡음에 대한 신호의 정확한 비율을 측정하기 어려운 단점이 있다. 이러한 단점을 보완하기 위하여 복잡한 통계 모델 및 다운샘플링이 되지 않은 웨이블릿 변환을 사용하는 것이 일반적이다. 본 논문에서는 잡음 영상에서 잡음의 정도를 측정할 수 있는 다수준 결정 모델을 이용한 잡음 제거 방법을 제안한다. 제안 방법은 잡음에 대한 신호의 비율을 다수준 값의 형태로 계산할 수 있기 때문에 직교 웨이블릿 변환으로 좋은 잡음 제거 성능을 나타낼 수 있다. 모의실험 결과를 통하여 본 논문의 방법이 직교 웨이블릿 변환을 사용한 최신의 잡음 제거 방법보다 PSNR 측면에서 평균적으로 0.ldB 정도 우수한 성능을 나타낸다는 것을 보여준다.

Keywords

References

  1. J. Amer. Statist. Assoc. v.90 no.432 Adapting to unknown smoothness via wavelet shrinkage D. L. Donoho;I. M. Jonhstone https://doi.org/10.2307/2291512
  2. IEEE Signal Processing Letters v.6 Low-complexity image denoising based on statistical modeling of wavelet coefficients M. K. Mihcak;I. Kozintsev;K. Ramchandran;P. Moulin https://doi.org/10.1109/97.803428
  3. IEEE Trans. Image Processing v.9 Spatially adaptive wavelet thresholding with context modeling for image denoising S. G. Chang;B. Yu;M. Vetterli https://doi.org/10.1109/83.862630
  4. Proc. IEEE Int. Conf. Acous., Speech and Signal Processing v.6 Spatially Adaptive statistical Modeling of Wavelet Image Coefficients and Its Application to Denosing M. K. Mihcak;I. Kozintsev;K. Ramchandran
  5. Proc. IEEE Int. Conf. on Image Processing Image denoising based on scale-space mixture modeling of wavelet coefficients J. Liu;P. Moulin
  6. IEEE. Trans. Image Processing v.46 Wavelet-based statistical signal processing using hidden Markov models M. S. Crouse;R. D. Nowak;R. G. Baraniuk https://doi.org/10.1109/78.668544
  7. IEEE. Trans. Image Processing v.10 no.7 Bayesian tree-structured image modeling using wavelet-domain hidden Markov models J. K. Romberg;H. Choi;R. G. Baraniuk https://doi.org/10.1109/83.931100
  8. Proc. IEEE Int. Conf. Acous. Speech and Signal Processing Hidden Markov Tree Modeling of Complex Wavelet Transforms H. Choi;J. Romberg;R. Baraniuk;N. Kingsbury
  9. Proc. SPIE v.3816 Bayesian tree structured image modeling using wavelet domain hidden Markov model J. K. Romberg;H. Choi;R. Baraniuk https://doi.org/10.1117/12.351328
  10. IEEE. Transaction on Image Processing v.6 no.4 Wavelet-besed image denoising using a Markov random field a priori model M. Malfiat;D. Roose https://doi.org/10.1109/83.563320
  11. IEEE. Transaction on Image Processing v.11 no.5 A joint inter- and intra scale statistical model for Bayesian wavelet based image denoising A. Pizurica;W. Ohulips;I. Lemahieu;M. Acheroy https://doi.org/10.1109/TIP.2002.1006401
  12. IEEE Signal Processing Letters v.9 no.12 Bivariate shrinkage with local variance estimation L. Sendur;I. W. Selesnick https://doi.org/10.1109/LSP.2002.806054
  13. Electron. Lett. v.37 no.11 Efficient wavelet based image denoising algorithm Z. Cai;T. H. Cheng;C. Lu;K. R. Subramanian https://doi.org/10.1049/el:20010466