摘要
噪声抑制是遥感影像处理中一个重要研究课题,但常用的去噪算法会造成细节损失。为有效抑制噪声,同时保护边缘,本研究在Perona-Malik扩散模型基础上,提出了一种新的基于方向信息测度和边缘隶属度的各向异性扩散滤波算法。本算法的核心思想是根据遥感影像在其均匀区域各向同性扩散而在边缘细节区域各向异性扩散的这种局部特征,将影像分为边缘区和非边缘区两个区域,对非边缘区采用常规Perona-Malik扩散方程完成噪声的滤除,而对边缘区采用基于方向信息测度的非线性扩散方法进行处理,在平滑去噪的同时对边缘进行修整,不仅可以很好保持边缘细节信息,而且可以对其进行增强。实验结果表明,该算法的峰值信噪比、均方误差、辐射分辨率等参数均优于常规算法,提高了遥感影像的等效视数,有效消除了影像中的相干噪声。算法具有良好的应用前景和实用价值。
Noise reduction is an important research topic for the remote sensing image processing, and the commonly used noise reduction methods usually cause a loss of the details. In order to effectively reduce the noise and keep the edge information intact at the same time, a new anisotropic diffusion algorithm based on the information measure and the edge membership is proposed. As the local feature of remote sensing image which has anisotropie diffusion in the heterogeneous area and isotropie diffusion in the homogeneous area, the core content of this algorithm is to divide the remote sensing image into two areas, the edge area and the non-edge area. While the conventional Perona-Malik diffusion equation is used into the non-edge area to filter the noise, and the nonlinear diffusion equation based on the information measure is used into the edge area to filter the noise and enhance the edge and details. The final results show that this algorithm＇s Peak Signal to Noise Ratio （F＇SNR）, Maximum Absolute Error （MAE） and radiometrie resolution are better than the traditional algorithms. Experimental results also prove that the algorithm can greatly enhance the equivalent number of looks of the remote sensing image and effectively inhibit the coherent noise of the remote sensing image. So this algorithm has practicality and potential application value.
出处
《科技导报》
CAS
CSCD
北大核心
2010年第1期69-73,共5页
Science & Technology Review
基金
江苏省资源环境信息工程重点实验室开放基金项目(20080105)
关键词
噪声抑制
各向异性扩散
方向信息测度
边缘隶属度
辐射分辨率
image noise inhibition
anisotropic diffusion
information measure
edge membership
radiometrie resolution
