基于高斯- 拉普拉斯滤波的增强局部对比度红外小目标检测算法

To address the problem of high false alarm rate of single-frame infrared small-target detection algorithm in low-altitude and complex backgrounds, a Laplacian of Gaussian (LOG) filter-based enhanced local contrast algorithm is proposed. First, the candidate target pixels are extracted quickly by LOG filtering, while the target is enhanced using pixel grayscale indexing. Then, the target saliency map is calculated based on the grayscale features of the target and the background in the local area. Finally, the target is extracted by adaptive threshold segmentation. Test datasets are constructed for different low-altitude complex scenarios, and the proposed algorithm is compared with the Top-Hat algorithm, Max-median algorithm, RLCM algorithm, IPI algorithm, and MPCM algorithm in terms of signal-to-noise ratio gain, background rejection factor, detection rate, false alarm rate, and computational efficiency. Results show that in different scenarios, the newly proposed algorithm not only has higher signal-to-noise ratio gain and background rejection factor, but also has higher detection rate, lower false alarm rate and higher computational efficiency than other algorithms, demonstrating the method’s effectiveness and robustness.