A LOG Filter Based Enhanced Local Contrast Algorithm to Detect Infrared Small Targets

Pengge MA; Hongguang WEI; Junling SUN; Ran TAO; Dongdong PANG; Tao SHAN; Zhiyong CAI; Zhaoyu LIU

doi:10.12382/bgxb.2021.0767

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A LOG Filter Based Enhanced Local Contrast Algorithm to Detect Infrared Small Targets

更新时间：2025-08-18

- A LOG Filter Based Enhanced Local Contrast Algorithm to Detect Infrared Small Targets
- Acta Armamentarii Vol. 44, Issue 4, Pages: 1041-1049(2023)
- 作者机构：
  
  1. 郑州航空工业管理学院智能工程学院, 河南郑州 450015
  2. 北京理工大学信息与电子学院, 北京 100081
  3. 中航工业通飞华南飞机工业有限公司, 广东珠海 519099
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2021.0767
  CLC：
- Received：12 November 2021，
  
  Published Online：25 July 2023，
  
  Published：28 April 2023
- 稿件说明：
移动端阅览
Pengge MA, Hongguang WEI, Junling SUN, et al. A LOG Filter Based Enhanced Local Contrast Algorithm to Detect Infrared Small Targets[J]. Acta Armamentarii, 2023, 44(4): 1041-1049.
DOI：

Pengge MA, Hongguang WEI, Junling SUN, et al. A LOG Filter Based Enhanced Local Contrast Algorithm to Detect Infrared Small Targets[J]. Acta Armamentarii, 2023, 44(4): 1041-1049. DOI： 10.12382/bgxb.2021.0767.

摘要

针对单帧图像红外小目标检测算法在低空复杂背景下虚警率高的问题

提出一种基于高斯-拉普拉斯(LOG)滤波的增强局部对比度算法来检测低空复杂背景下的红外小目标。通过LOG滤波运算快速提取候选目标像素

同时通过像素灰度指数运算增强目标。根据局部区域目标与背景的灰度特征计算目标显著图

通过自适应阈值分割提取目标。针对不同的低空复杂场景

构建了测试数据集

从信噪比增益、背景抑制因子、检测率、虚警率及算法计算效率方面将所提算法与Top-Hat算法、Max-median算法、RLCM算法、IPI算法及MPCM算法进行对比分析。实验结果表明

在不同场景中

所提算法相较于对比算法不仅具有较高的信噪比增益和背景抑制因子

而且具有较高的检测率、较低的虚警率和较高计算效率

验证了该算法的有效性和鲁棒性。

Abstract

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.

关键词

Keywords

references

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