The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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Wavelet Based Non-Local Means Filtering for Speckle Noise Reduction of SAR Images

SAR 영상에서 웨이블렛 기반 Non-Local Means 필터를 이용한 스펙클 잡음 제거

  • Received : 20100200
  • Accepted : 20100400
  • Published : 2010.06.30
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Abstract

This paper addresses the problem of reducing the speckle noise in SAR images by wavelet transformation, using a non-local means(NLM) filter originated for Gaussian noise removal. Log-transformed SAR image makes multiplicative speckle noise additive. Thus, non-local means filtering and wavelet thresholding are used to reduce the additive noise, followed by an exponential transformation. NLM filter is an image denoising method that replaces each pixel by a weighted average of all the similarly pixels in the image. But the NLM filter takes an acceptable amount of time to perform the process for all possible pairs of pixels. This paper, also proposes an alternative strategy that uses the t-test more efficiently to eliminate pixel pairs that are dissimilar. Extensive simulations showed that the proposed filter outperforms many existing filters terms of quantitative measures such as PSNR and DSSIM as well as qualitative judgments of image quality and the computational time required to restore images.

본 논문에서는 일반 영상의 가우시안 잡음 제거에 유용한 Non-Local Means 필터를 이용하여 웨이블렛 도메인 상에서 SAR 영상의 스펙클 잡음제거 방법을 제안하고자 한다. 먼저 승법 잡음인 스펙클 잡음을 로그를 취해 가법 잡음으로 변환한 후 웨이블렛 분해하고 고주파 혹은 저주파 서브밴드에 따라 Non-Local Means 필터와 웨이블렛 임계값 처리(wavelet thresholding)를 선택적으로 적용하고 지수형태를 취해 원영상으로 복원함으로서 잡음을 제거한다. 또한, Non-Local Means 필터의 단점인 수행시간을 단축시키기 위해 통계적 t-검정을 이용하여 개선하고자 한다. 영상실험을 통한 성능평가 결과 제안된 필터는 정성적인 비교와 PSNR과 DSSIM을 통한 정량적인 비교 모두 기존의 필터보다 우수한 성능을 보였다. 통계적 t-검정을 이용해 개선된 방법은 빠른 계산 속도와 더 나은 성능을 나타냈다.

Keywords

Acknowledgement

Supported by : 한국연구재단

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