对比红外小目标检测方法和其它目标检测方法, 由于低信噪比、低对比度、小尺寸、缺乏目标的形状和纹理信息等多种因素, 尤其是在复杂背景条件下, 红外小目标的检测会更加的困难.在实践中, 一种基于同组过滤器(Peer Group Fileter, PGF), 二维经验模式分解(Bidimensional Empirical Mode Decomposition, BEMD)和局部逆熵(Local Inverse Entropy, LIE)的新型红外小目标检测方法被提出来, 以解决前面所提到的问题.其中PGF被用来消除噪声和改善初始图像的信噪比; BEMD算法可以有效地估计背景并将背景从原始图像中移除; 而LIE的主要作用是分解本征模态函数(Intrinsic Mode Function, IMF).实验结果表明, 新的方法可以有效且准确地提取小目标.

Compared with other targets, it is more difficult to detect infrared small targets due to several aspects such as the low signal to noise ratio, low contrast, small size, the lack of shape and texture information of the targets, especially under complex background. A novel infrared small target detection method based on peer group filter (PGF), bi-dimensional empirical mode decomposition (BEMD) and local inverse entropy (LIE) was proposed to overcome these difficulties. The PGF is implemented to remove noise and improve signal-to-noise ratio of the initial image. The proposed BEMD algorithm is able to estimate background effectively, which gets target image by removing background from original image and segmenting the Intrinsic Mode Functions (IMFs) by local inverse entropy. Experimental results demonstrated that the novel method can extract the small targets validly and accurately.