Title :
Despeckling SAR image using nonsubsampled directionlets combining with GSM model
Author :
Gao, Qingwei ; Zhang, Chi ; Lu, Yixiang
Author_Institution :
Key Lab. of Optio-Electron. Inf. Acquisition & Manipulation, Anhui Univ., Hefei, China
Abstract :
As speckle noise suppression is important for Synthetic aperture radar (SAR) images processing, this paper presents an approach for SAR image despeckling based on nonsubsampled directionlets. Firstly, images are partitioned into subbands using nonsubsampled directionlets. Then, the coefficients of subbands are modeled with Gaussian scale mixtures (GSM). Besides, for reducing the speckle noise, coefficients are estimated by Bayes least square estimation. Lastly, to evaluate the performance of this despeckling method, four performance evaluation parameters are used. Experimental results show that this method performs better in comparison with the spatial filter and standard wavelet transform not only in speckle suppression, but also in detail preservation.
Keywords :
Bayes methods; Gaussian processes; image denoising; least squares approximations; radar imaging; synthetic aperture radar; wavelet transforms; Bayes least square estimation; GSM model; Gaussian scale mixtures; SAR image despeckling; nonsubsampled directionlets; spatial filter; speckle noise suppression; synthetic aperture radar; wavelet transform; Filtering; GSM; Noise; Speckle; Synthetic aperture radar; Wavelet transforms; Gaussian scale mixtures model; SAR image; despeckling; nonsubsampled directionlet transform;
Conference_Titel :
Image and Signal Processing (CISP), 2010 3rd International Congress on
Conference_Location :
Yantai
Print_ISBN :
978-1-4244-6513-2
DOI :
10.1109/CISP.2010.5647222