针对重构算法影响压缩成像目标检测效率和结果的问题, 提出一种二维非重构自适应阈值的红外弱小目标检测算法。基于Hadamard矩阵构建的二维观测模型, 利用Hadamard矩阵的特性对压缩差分图像进行解压缩, 直接解码目标的空域特征, 并利用改进的自适应阈值法对解码后的图像进行目标检测, 避免了重构带来的存储空间和运算时间的浪费。仿真实验表明: 在单目标和多目标的情况下, 该算法都可以有效检测目标, 并在检测率、虚警率和运算时间等指标上具有优越性能, 为压缩感知红外弱小目标检测的工程应用提供新的思路和有效算法。

A two-dimensional non-reconstruction adaptive threshold algorithm aiming at infrared small target detection was proposed, for the purpose of decreasing the reconstruction algorithms′ negative influence on target detection′s efficiency and results. Aiming at the two-dimentional measurement model which constructed by Hadamard matrix, the compressive subtract image was analyzed by means of Hadamard′s property in order to decode target′s characteristics in space domain directly. Then the decoded image was detected by utilizing the advanced adaptive threshold method, which avoided the waste of memory space and operation time caused by traditional reconstruction algorithms. Simulation experiment demonstrates that the proposed model can detect the targets on the condition of both single and multiple targets, and has superiorities on detection rate, false alarm rate and operation time than the traditional detection algorithm after reconstruction. It provides a new idea and efficient algorithm for the application of compressive sensing infrared small target detection in engineering.