基于Curvelet域自适应数学形态学降噪的含噪图像盲分离方法

Blind Separation of Noisy Image Based on Adaptive Morphological De-noising in Curvelet Transform Domain

针对含有噪声情况下的盲分离问题,提出一种基于Curvelet域自适应数学形态学降噪的含噪图像盲分离方法.该方法在对含噪混合图像进行Curvelet多尺度几何分析的基础上,根据Curvelet变换域信号稀疏的特点,采用位置相关自适应数学形态学降噪算子进行降噪,选取最稀疏的子带图像寻求分离矩阵,进而实现全局分离.仿真结果显示,该方法对于含噪图像的盲分离具有良好的性能.

According to traditional blind source separation algorithm without taking into account noise, a new algorithm for blind separation of noisy image is proposed based on adaptive morphology in the curvelet transform domain. Curvelet transform has good performance in sparseness. Noisy image can be analyzed with curvelet transform, and de-noised with adaptive de-noising algorithm based on mathematical morphology. The separation matrix can be estimated by selecting the sparsest sub-band image. The mixed image can be separated thoroughly. Simulation results show that the proposed algorithm can achieve a better performance for blind source separation of noisy images.