基于局部对比度机制的红外弱小目标检测算法

针对复杂背景和低信杂比条件下的红外弱小目标检测难题，提出了一种基于局部对比度机制的红外弱小目标检测方法。该方法提出了一个包含中心层、中间层和最外层的3层窗口，可以使用单尺度计算完成不同尺度弱小目标的检测。首先，对中心层引入匹配滤波思想，有针对性地增强真实目标；同时，提出最接近滤波原则，对最外层进行背景估计，以缓解目标靠近边缘时的检测难题；然后，在目标增强结果与背景估计结果之间进行比差联合的对比度计算，达到同时增强目标和抑制背景的目的；最后，通过自适应阈值分割，提取真实目标。实验结果表明，相比现有算法而言，该算法可更好地增强目标、抑制复杂背景，且原理简洁易实现，可有效减少运算量。

Infrared Small Dim Target Detection Based on Local Contrast Mechanism

A method for infrared(IR)small dim target detection based on a local contrast mechanism is proposed to solve the problem of IR small dim target detection under a complex background and low signal-to-clutter ratio(SCR).A three-layer window consisting of an inner layer,a middle layer,and an outer layer is proposed,so that targets of different scales can be detected using only single-scale calculations.First,the matched filter is applied to the inner layer to enhance the true target purposefully,and the max-close principle is proposed to estimate the background of the outer layer,so that detection becomes easier when the target is near the background edge.Then,the ratio-difference joint local contrast measure is calculated between the enhanced target and the estimated background to enhance the true target and suppress the complex background simultaneously.Finally,an adaptive threshold operation is used to extract the true target.Experimental results show that compared to some existing algorithms,the proposed algorithm can enhance the true target and suppress complex background better,and its principle is simple yet suitable for implementation and can effectively reduce the amount of calculation.