Curvelet变换域自适应收缩图像去噪

Journal of Applied Sciences

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Adaptive Shrinkage Image Denoising in Curvelet Domain

DENG Cheng-zhi 1, CAO Han-qiang 1, WANG Sheng-qian 2

1. Department of Electronic & Information, Huazhong University of Science & Technology, Wuhan 430074, China;
2. Key Lab of Optic-Electronic & Communication, Jiangxi Science & Technology Normal College, Nanchang 330013, China

Received:2007-05-13 Revised:2007-08-29 Online:2008-01-31 Published:2008-01-31

Abstract

Abstract: A nonparametric Bayesian estimator for image denoising in the curvelet domain is studied. A prior model, named normal inverse Gaussian (NIG), is imposed on the curvelet coefficients designed to capture the sparseness of the curvelet expansion. Based on this, a NIG-based maximum a posteriori (MAP) estimator is designed. By estimating the model parameters of curvelet subband coefficients, a MAP-based subband adaptive shrinkage image denoising is realized. Simulation is carried out to show effectiveness of the denoiser. Experimental results show that the proposed method can effectively reduce noise while keep details.

Key words: curvelet transform, normal inverse Gaussian (NIG), maximum a posteriori, image denoising

DENG Cheng-zhi;CAO Han-qiang;WANG Sheng-qian . Adaptive Shrinkage Image Denoising in Curvelet Domain[J]. Journal of Applied Sciences.

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Adaptive Shrinkage Image Denoising in Curvelet Domain

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