中文摘要：

针对单尺度固定函数的滤波器难以有效剔除杂波和提高小弱目标检测性能的不足，文中研究建立多尺度自适应稀疏字典，提出了一种多尺度自适应形态稀疏字典检测小弱目标方法。首先根据图像信号内容建立多尺度自适应形态稀疏字典，并将图像信号在多尺度稀疏字典中进行稀疏分解；然后在分析小原子稀疏表示系数的基础上建立稀疏表示系数直方图，并利用指数函数拟合小尺度原子的稀疏表示系数直方图；最后，根据指数函数拟合参数在杂波、噪声和目标表现出的差异检测小弱目标。该多尺度稀疏字典利用大尺度原子描述图像背景杂波，小尺度原子捕获图像细小特征。实验结果表明，与小波算法和Contourlet算法相比，文中方法能更为有效地抑制背景杂波，减少背景残留，从而提高小弱目标检测性能。

英文摘要：

To overcome the deficiency that the fixed filter with single-scale cannot effectively remove the clutter and improve the performance of dim target detection, a dim target detection method based on a multi-scale adaptive sparse dictionary was proposed in this paper. Firstly, an adaptive multi-scale sparse dictionary was learned based on the sparse coding theory, and the sparse coefficient of the original image at different scales was decomposed. Then exponential fitting function was adopted to fit the statistical sparse representation coefficient histogram at the small-scale atom. Finally, the differences in the exponential fitting function for the target and noise in the multi-scale adaptive sparse dictionary could be＆amp;nbsp;extracted and applied to detect the target. This sparse dictionary contained the atoms with different scale, the large-scale atom can describe the background of the image, and the small-scale atom can capture the subtle feature. The results show that this proposed method based multi-scale adaptive sparse dictionary could suppress the clutter more greatly and improve the performance of dim target detection more effectively compared to wavelet and Contourlet method.