基于Zernike-Facet模型和总体最小二乘的弱小目标检测

New Small Target Detection Algorithm via Zernike-Facet Model and the Total Least Squares

弱小目标一般是图像局部区域的极值点。针对这个特点,依据二元三次函数的极值理论,该文提出了一种新的弱小目标候选点的检测方法。发展了一种新的图像局部灰度拟合模型,即Zernike-facet模型,模型参数的求解采用比最小二乘(LS)抗噪能力更强的总体最小二乘(TLS)算法。新检测方法通过Zernike-facet模型和TLS对原始图像中每一个像素的局部区域进行曲面拟合,然后在拟合曲面上提取极值点作为目标候选点。仿真表明,新方法在抑制噪声上优于其他常用方法。可见光/红外图像小目标检测实验也证实了新方法的有效性。

In general, small targets always are the extremum pixels in image local area. Based on the feature of targets, a new small target detection algorithm is presented based on the extremum theory for bi-variate cubic function. In this paper, a new model is developed named Zernike-facet model, which is used to fit local image intensity. And coefficients of the model are solved by the Total Least Squares （TLS） method, which performance in resisting noise is better than the Least Squares （LS） method. Then the new small target detection algorithm is proposed. The new algorithm used the Zernike-facet model and the TLS to fit image local intensity surface, and then those extremum points are extracted as targets. The simulations show that the new method is better in resisting noise than others. Several target detection experiments are carried out on visible /infrared image. The results demonstrate that the proposed method is efficient.