The Journal of Korean Institute of Communications and Information Sciences (한국통신학회논문지)

The Korean Institute of Commucations and Information Sciences (한국통신학회)
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Multiple Decision Model for Image Denoising in Wavelet Transform Domain

웨이블릿 변환 영역에서 영상 잡음 제거를 위한 다중 결정 모델

  • Published : 2004.07.01
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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

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