摘要
提出一种利用图像在曲波域上局部统计特性的自适应去噪方法。首先在最小线性均方差(LMMSE)准则下,推导出曲波系数在局部区域的恢复公式;为进一步精确估计理想曲波系数的局部方差,提出利用尺度空间和子带内的相关性,即利用粗尺度下小波系数的局部方差预测精细尺度下相应位置的曲波系数为噪声的概率,以及常规估计下的曲波系数的局部方差是否小于门限值判断其是否为噪声;然后以这些局域窗内非噪声成分系数估计理想曲波系数的方差。实验表明,本算法与传统算法相比,图像质量有进一步地改善,尤其是对细节丰富的图像表现更为突出。
An adaptive image denoising method is presented which utilizes the local statistical property of curvelet coefficients. Firstly, under the rule of LMMSE the restoration formula of the curvelet coefficients are deduced, the method exploits the correlation of inter-scale and intra-subband. The local variation of a eurvelet coefficient in the coarse scale is used to predict whether the coefficient in the next fine scale is noise component, which exploits the first correlation. As well, by threshold, the regular estimation of local variance is used to predict whether the coefficient is noise component, which exploits the second correlation. Then ideal local variance of a curvelet is estimated from those coefficients which are not the noise components in the local window. The experiments show that, compared with traditional method, the algorithm makes a great improvement of image quality, especially for the image with much detailed information.
出处
《武汉理工大学学报》
CAS
CSCD
北大核心
2009年第3期1-5,共5页
Journal of Wuhan University of Technology
基金
国家自然科学基金(60672137)
教育部高校博士点基金(20060497015)
