采用核Rayleigh商二次相关滤波器的星图自适应杂波抑制

为了实现星图中弱小星点目标的检测，提出了一种基于核Rayleigh二次相关滤波器（KRQQCF）的星图自适应杂波抑制方法.采用星图模拟方法随机产生视轴指向，根据二维高斯模型产生训练样本，提取改进的加速鲁棒特征(SURF)，通过训练学习构建KRQQCF.为了快速检测目标，对待测图像首先用频域残差法检测星图中星点可能存在的显著性区域，然后提取该显著区域改进的5维SURF特征.最后，通过KRQQCF识别目标，有效抑制杂波及噪声，提高星图的信噪比.实验结果表明，该算法快速、有效、可靠.

Star Image Adaptive Clutter Suppression Using Kernel Rayleigh Quotient Quadratic Correlation Filter

In order to detect small star point targets in star images, an adaptive clutter suppression algorithm based on kernel Rayleigh quotient quadratic correlation filter was proposed. The star image simulation method was adopted to generate optical axis point randomly, produce training samples according to the two dimensional Gaussian model, extract improved speed-up robust features(SURF), and learn to build kernel Rayleigh quotient quadratic correlation filter(KRQQCF). In order to detect the target quickly, for the image to be detected, the spectral residual method was used to detect salient regions probably containing targets. Then the improved 5-dimension SURF feature of the salient regions was extracted. Finally, the target was recognized using KRQQCF, and the clutter and noise were suppressed effectively which improved the SNR. Experimental results indicate that the proposed algorithm is fast, effective and robust.