形态学和小波域滤波级联的杂波抑制算法研究

为保留目标的同时尽可能多地抑制图像背景杂波,在研究形态学滤波、空域平滑滤波、小波域平滑滤波以及它们级联的背景杂波抑制算法的基础上,提出形态学滤波和小波域平滑滤波级联的杂波抑制算法。该算法先对含有微弱目标和白高斯噪声的原始图像进行空域形态学滤波,然后对形态学滤波结果进行小波变换,在小波域中对各小波系数分别平滑滤波,用滤波前的小波系数减去滤波后的小波系数得到差值系数,对差值系数再进行逆小波变换。仿真测试表明,提出的算法在得到的残差图像中目标邻域信杂比(SCNR)平均增益比空域形态学滤波算法和空域平滑滤波算法等均有较明显的提高,为后续的目标检测与跟踪提供了良好的保障。

Analysis of Clutter Suppression based on the Cascade of Morphological Filtering and Wavelet Smoothing

In order to suppress image background clutter as much as possible while reserve targets,the clutter suppression algorithm based on the cascade of morphological filtering and wavelet smoothing is proposed in this thesis on the basis of the research of morphological filtering,spatial smoothing,wavelet smoothing and their cascaded background clutter suppression algorithms.The original image with dim targets and white Gaussian noise is processed with morphological filtering.Wavelet transform is performed on the morphological filtering result to produce wavelet coefficients,and wavelet coefficients are smoothed in the wavelet domain.The difference process is operated between the before filtering and after filtering wavelet coefficients.The inverse wavelet transform is performed on the difference coefficients.Computer simulation results show that the average gain of the target＇s neighborhood signal-to-clutternoise ratio （SCNR） in the obtained residual image can be improved obviously by the method in this paper compared with that by spatial morphological filtering or spatial smoothing alone.The algorithm in this paper provides good support for the subsequent target detection and tracking.