Wavelet-based multiscale anisotropic diffusion for speckle reduction and edge enhancement

Yi Wang; Ruiqing Niu; Ke Wu

doi:10.1117/12.832844

30 October 2009 Wavelet-based multiscale anisotropic diffusion for speckle reduction and edge enhancement

Yi Wang, Ruiqing Niu, Ke Wu

Proceedings Volume 7494, MIPPR 2009: Multispectral Image Acquisition and Processing; 74940I (2009) https://doi.org/10.1117/12.832844
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

Abstract

In order to improve signal-to-noise ratio (SNR) and image quality, this paper introduces a wavelet-based multiscale anisotropic diffusion algorithm to remove speckle noise and enhance edges. In our algorithm, we use the tool of wavelet to construct a linear scale-space for the speckle image. Due to the smoothing functionality of the scaling function, the wavelet-based multiscale representation of the speckle image is much more stationary than the raw speckle image. Noise is mostly located in the finest scale and tends to decrease as the scale increases. Furthermore, a robust speckle reduction anisotropic diffusion (SRAD) is to be proposed and we perform the improved SRAD on the stationary scale-space rather than on the rough speckle image domain. Qualitative experiments based on a speckle Synthetic aperture radar (SAR) image show the elegant characteristics of edge-preserving filtering versus the traditional adaptive filters. Quantitative analyses, based on the first order statistics and Equivalent Number of Looks, confirm the validity and effectiveness of the proposed algorithm.

Citation Download Citation

Yi Wang, Ruiqing Niu, and Ke Wu "Wavelet-based multiscale anisotropic diffusion for speckle reduction and edge enhancement", Proc. SPIE 7494, MIPPR 2009: Multispectral Image Acquisition and Processing, 74940I (30 October 2009); https://doi.org/10.1117/12.832844

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KEYWORDS

Speckle

Image filtering

Anisotropic diffusion

Digital filtering

Diffusion

Synthetic aperture radar

Wavelets

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