Letters: Denoising MMW image using the combination method of contourlet and KSC shrinkage

Authors:
Li Shang;Pin-gang Su;Tao Liu
Affiliations:
Department of Electronic Information Engineering, Suzhou Vocational University, Suzhou 215104, Jiangsu, China and Department of Automation, University of Science and Technology of China, Hefei, An ...;Department of Electronic Information Engineering, Suzhou Vocational University, Suzhou 215104, Jiangsu, China and State Key Lab of Millimeter Waves, Southeast University, Nanjing 210096, Jiangsu, ...;Department of Electronic Information Engineering, Suzhou Vocational University, Suzhou 215104, Jiangsu, China
Venue:
Neurocomputing
Year:
2012

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Abstract

A new denoising method of milli-meter wave (MMW) image using contourlet and kurtosis based sparse coding (KSC) is proposed in this paper. KSC is a high-order statistical method and can efficiently extract image feature coefficients. Contourlet method has the decomposition property of orientation and the energy variation for images. Further, using the shrinkage threshold that is determined by the sparse prior distribution of feature coefficients extracted in the contourlet transform field, the unknown noise contained in MMW image can be reduced efficiently. In test, an artificial MMW image and a true MMW are respectively used to validate our method, further, compared this method with other denoising methods, the simulation results show this method proposed here can obtain the better quality of image restoration.