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| Denoising Millimeter Wave Image with Contourlet and Sparse Coding Shrinkage |
| SHANG Li1,SU Pin-gang1,2,Zhou Chang-xiong1 |
1.Department of Electronic Information Engineering, Suzhou Vocational University, Suzhou, Jiangsu 215104, China
2.State Key Laboratory of Millimeter Waves, Southeast University, Nanjing, Jiangsu 210096, China |
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Abstract Combined the high-order statistical property of the sparse coding, which is based on kurtosis measurement (KSC) and the property of the contourlet's composing orientation and the energy variation, a new denoising method of millimeter wave image, which is based on contourlet and KSC shrinkage technique, is proposed. Kurtosis based Sparse Coding algorithm is an efficient image feature extraction method, which can model the human primary visual system. According to the sparse prior distribution knowledge of feature coefficients extracted, the shrinkage threshold can be determinate. Using this shrinkage technique in the contourlet transform field, the unknown noise contained in millimeter wave image can be reduced efficiently. And utilizing the relative single noise ratio criterion to measure the quality of the image denoised, the simulation experimental results show that comparing with other denoising methods such as sparse coding shrinkage, contourlet denoising and wavelet soft threshold shrinkage, this method proposed here can obtain the better quality of image restoration.
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2012, Vol. 33 
