基于非线性局部滤波的红外小目标检测方法

为提高复杂环境下红外小目标的检测效率，将图像分为平坦区域、边缘区域和小目标区域三种区域，并针对三种成分的特点，提出基于拉普拉斯金字塔的非线性局部滤波检测方法。首先将图像进行高斯金字塔分解，将高斯低通金字塔与原图像尺寸匹配后，相减并进行阈值操作，抑制平坦区域；其次将标记像素灰度值与其周围环域均值的最小差作为指标，滤除边界区域；最后将非线性局部滤波结果生成相应的拉普拉斯金字塔各层系数，重构得到高对比度的检测图像，利用邻域特点剔除孤立噪声点并通过简单阈值标记红外小目标。实验结果表明：与现有其他算法相比，该检测算法能够对复杂背景有效抑制，检测速度快。

Infrared small target detection method based on nonlinear local filter

In order to improve the efficiency of infrared small target detection against complex background, the image was decom-posed into three regions:flat region, edge region and small target region. A method of nonlinear local filter detection using the Lapla-cian pyramid was presented based on each character of the three components. Firstly, Gaussian pyramids were built for the image, each level was subtracted from the original image with matching size, and the flat region was restrained by simple threshold operation. Secondly, the minimum difference between the marked pixel gray value and the mean value of the hollow annular region was used as quota to filter out the edge region. At last, each layer coefficient of the Laplacian pyramid was generated from the results of nonlinear local filtering and then a high-contrast detection image was reconstructed. The isolated noise points were removed based on the charac-ter of the neighborhood and the infrared small target was marked by simple threshold operation. Compared with other existing methods, the experimental results show that this method can effectively restrain complex background and the detection speed is fast.