应用轮廓波和稀疏编码收缩法消噪毫米波图像

结合以峭度为稀疏标准的稀疏编码算法的高阶统计特性以及轮廓波分解的方向性和能量变化特性,提出了一种新的基于轮廓波和稀疏编码收缩技术的毫米波图像消噪方法。稀疏编码是一种有效的模拟视觉系统的图像特征提取方法,根据提出的特征系数的稀疏先验分布知识,能够自适应地确定收缩阈值。把该收缩技术应用到轮廓波变换域,能够很好地减弱毫米波图像中的未知噪声。采用相对信噪比评判消噪图像的质量,仿真实验表明,与标准稀疏编码收缩方法、轮廓波变换域降噪方法以及小波软阈值收缩方法相比,该降噪方法能够获得较好的图像恢复质量。

Denoising Millimeter Wave Image with Contourlet and Sparse Coding Shrinkage

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.