Cui Yi, Lei Tao, Chen Guiting, Zhang Yunjing, Zhang Gang, Hao Xuying
Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China.
National Laboratory on Adaptive Optics, Chengdu 610209, China.
Sensors (Basel). 2025 Jul 15;25(14):4405. doi: 10.3390/s25144405.
The robust detection of small targets is crucial in infrared (IR) search and tracking applications. Considering that many state-of-the-art (SOTA) methods are still unable to suppress various edges satisfactorily, especially under complex backgrounds, an effective infrared small target detection algorithm inspired by modified fast saliency and the weighted guided image filter (WGIF) is presented in this paper. Initially, the fast saliency map modulated by the steering kernel (SK) is calculated. Then, a set of edge-preserving smoothed images are produced by WGIF using different filter radii and regularization parameters. After that, utilizing the fuzzy sets technique, the background image is predicted reasonably according to the results of the saliency map and smoothed or non-smoothed images. Finally, the differential image is calculated by subtracting the predicted image from the original one, and IR small targets are detected through a simple thresholding. Experimental results on four sequences demonstrate that the proposed method can not only suppress background clutter effectively under strong edge interference but also detect targets accurately with a low false alarm rate.
小目标的稳健检测在红外（IR）搜索与跟踪应用中至关重要。鉴于许多先进（SOTA）方法仍无法令人满意地抑制各种边缘，特别是在复杂背景下，本文提出了一种受改进的快速显著性和加权引导图像滤波器（WGIF）启发的有效红外小目标检测算法。首先，计算由转向核（SK）调制的快速显著性图。然后，加权引导图像滤波器使用不同的滤波半径和正则化参数生成一组保边平滑图像。之后，利用模糊集技术，根据显著性图和平滑或未平滑图像的结果合理预测背景图像。最后，通过从原始图像中减去预测图像来计算差分图像，并通过简单的阈值处理来检测红外小目标。在四个序列上的实验结果表明，该方法不仅能在强边缘干扰下有效抑制背景杂波，还能以低误报率准确检测目标。
