由于红外弱小目标尺度小、能量弱，所以抑制背景以增强目标使后期检测跟踪性能得到保障是关键的目标检测技术环节。为了提高梯度倒数滤波算法对杂波纹理的抑制能力，减少差分图像中残留纹理对目标的干扰，本文提出了自适应梯度倒数滤波算法(AGRF)。AGRF 算法通过分析背景区域、杂波边缘纹理、目标的分布特性和统计数字特征来确定邻域像素间相关性的自适应联合判定阈值和自适应相关度系数函数，然后联合相关度系数函数和梯度倒数系数来确定自适应梯度倒数滤波器的元素值。实验结果表明，在具有相同目标增强性能的前提下，AGRF 算法相比传统梯度倒数滤波算法对杂波边缘纹理的敏感度明显降低。相比九种对比算法，AGRF 算法能够在背景抑制和目标增强这两者之间取得更好的性能平衡。

Due to the small scale and weak energy of the infrared dim small target, the background must be suppressed to enhance the target in order to ensure the performance of detection and tracking of the target in the later stage. In order to improve the ability of gradient reciprocal filter to suppress the clutter texture and reduce the interference of the residual texture to the target in the difference image, an adaptive gradient reciprocal filtering algorithm(AGRF) is proposed in this paper. In the AGRF, the adaptive judgment threshold and the adaptive relevancy coefficient function of inter-pixel correlation in the local region are determined by analyzing the distribution characteristics and statistical numeral characteristic of the background region, clutter texture, and target. Then the element value of the adaptive gradient reciprocal filter is determined by combining the relevancy coefficient function and the gradient reciprocal function. Experimental results indicate that the sensitivity of the AGRF algorithm to the clutter texture is significantly lower than that of the traditional gradient reciprocal filtering algorithm under the premise of the same target enhancement performance. Compared with the other nine algorithms, the AGRF algorithm has better signal- to-noise ratio gain (SNRG) and background suppress factor (BSF).助项目(2019AC20147)