It is always a challenging task to detect a small target with low signal-to-noise ratio under complex background in infrared images. To address this problem, an effective algorithm based on background subtraction is proposed. First, we add the gradient feature into the kernel regression model to acquire an edge-preserving background estimation. The smoothing matrix of the kernel function is reestablished by a rotation angle and an elongation scale. Further, a multiscale first-order directional derivative filter is presented to calculate these factors adaptively. Second, to segment the real target from the subtracted image, we model the imaging process of the small target using the point spread function of the optical system. According to the analysis of the imaging size and the energy distribution of target, an energy concentration criterion is constructed and used for target extraction. Finally, comparison of experimental results demonstrates that the proposed algorithm achieves robust performances on background suppression and extracts the target accurately with a high detection probability and low false alarm rate.

中文翻译：

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红外弱小目标检测的自适应核回归和能量集中判据

在红外图像中检测复杂背景下低信噪比的小目标始终是一项具有挑战性的任务。针对这一问题，提出了一种基于背景减除的有效算法。首先，我们将梯度特征添加到核回归模型中以获得边缘保留的背景估计。核函数的平滑矩阵通过旋转角度和伸长尺度重新建立。此外，提出了一个多尺度一阶方向导数滤波器来自适应地计算这些因素。其次，为了从减影图像中分割真实目标，我们使用光学系统的点扩散函数对小目标的成像过程进行建模。根据成像尺寸和目标能量分布分析，构建能量集中准则并用于目标提取。最后，实验结果对比表明，该算法在背景抑制方面具有稳健的性能，能够准确提取目标，检测概率高，误报率低。