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文中提出的算法在笔记本电脑上实现,电脑配置为:Intel Core i5,主频2.50 GHz,内存为4.00 GB,32位操作系统。操作系统为Windows 7,开发软件为Matlab2012。
实验采用18帧存在弱小红外目标的图像进行有效性检测分析,其中,地面背景图像1帧,海天交界背景5帧,海洋背景1帧,天空背景11帧。滑动图像块大小为12×12。采用局部均值信噪比、局部均值信噪比增益和背景抑制因子衡量算法 [ 17] 对红外弱小目标的检测能力。计算公式为:
式中:mean(·)为求均值的函数;
${{{I}}_{{\rm{Local}}}}$ 为算法处理后局部区域的图像函数;${{\bar{I}}_{{\rm{Local}}}}$ 为算法处理后局部区域的图像均值;${\sigma _{{\rm{Local}}}}$ 为算法处理后局部区域的图像灰度离散度;${{{P}}_{{\rm{SNR}}}}$ 为算法处理后局部区域信噪比值;${{{P}}_{{\rm{SNR0}}}}$ 为算法处理前局部区域信噪比值;${\rm{GAI}}{{\rm{N}}_{{\rm{SNR}}}}$ 为局部均值信噪比增益;BSF为背景抑制因子,滤波前的噪声比滤波后的噪声。从实验结果如 表1~ 4和 图4~ 图7所示,文中提出的方法比中值滤波方法的效果好很多。与参考文献[ 18]提出的方法相比,在大部分情况下效果更好。通过观察3种方法的二值图可知,文中提出方法的检测虚警率极低,而中值滤波方法及文献提出的方法虚警率较高,对目标检测效果较差。从4组实验的三维灰度曲线可知,目标基本湮灭在起伏的背景中,因此,目标比较难以检测,3种方法抑制背景噪声的能力相当,但文中提出的方法对目标的增强效果显著,有利于检测目标。
Picture 1 SCR SCRgain BSF Median filter method 7.5 5.7 3 Ref. [ 18] method 6.9 5.3 2 Proposed method 10.2 7.8 2.7 Table 1. Experimental results 1
Picture 1 SCR SCRgain BSF Median filter method 13.1 9.8 2.9 Ref. [ 18] method 9.0 6.8 3.5 Proposed method 13.6 10.3 2.8 Table 2. Experimental results 2
Picture 1 SCR SCRgain BSF Median filter method 6.2 4.7 2.4 Ref. [ 18] method 8.1 6.2 2.6 Proposed method 9 6.9 2.6 Table 3. Experimental results 3
Picture 1 SCR SCRgain BSF Median filter method 9.9 3.2 2 Ref. [ 18] method 6.2 2 1.8 Proposed method 11 3.5 1.8 Table 4. Experimental results 4
由 图4~ 图7可知,该方法用于导弹末制导及红外侦察预警中,应用效果较好,可在海面、天空背景下有效检测弱小目标,提高末制导的目标检测概率,提升目标检测的侦察预警距离,为告警拦截系统争取反应时间。
Detection of dim and small infrared targets based on the most appropriate contrast saliency analysis
doi: 10.3788/IRLA20200377
- Received Date: 2020-09-24
- Rev Recd Date: 2020-11-12
- Available Online: 2021-05-12
- Publish Date: 2021-04-30
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Key words:
- infrared image sequence /
- dim and small target detection /
- significance analysis /
- optimal contrast
Abstract: Aiming at the current situation of dim target detection in infrared image, a dim and small target detection method based on the most appropriate contrast saliency analysis was proposed. In sliding serial port, the non-linear processing technology was used to process the image, which avoided the saliency produced by traditional saliency analysis algorithm when processing the image at the scene edge. The problem of value interference does not affect the ability of target extraction in the target area. A large number of hardware-in-the-loop simulation experiments were carried out. The results show that although the proposed method can not improve significantly in the background suppression factor, the performance of target detection in the two indicators of mean signal-to-noise ratio and signal-to-noise ratio gain of the proposed method are significantly enhanced. And among the three methods of image processing, the effect is the best.