Detection of dim and small infrared targets based on the improved singular value decomposition
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摘要: 为了克服传统的基于奇异值分解的目标检测方法存在目标强度变弱的不足之处,采用改进的奇异值分解方法用于红外弱小目标检测。根据奇异值分解的性质,对其中目标贡献最大的中序部分奇异值进行了非线性修正的改进,并将其它奇异值设置为零后通过重构图像得到背景抑制后的目标图像。结果表明,该方法不仅能够保存和增强目标能量,提高目标信号的信杂比和对比度,而且还能得到很好的背景抑制效果。Abstract: In order to solve the problem of target strengh weakness of traditional target detection method based on singular value decomposition (SVD), an improved SVD algorithm was proposed for background suppression in dim and small infrared target detection. According to the nature of SVD, nonlinear transformation was adopted to improve the middle order part of image singular values for the largest contribution to the goal. And then, the other singular value was set to zero,finally the target image was obtained by reconstructing image. The experimental results show that the proposed method could preserve and enhance the target signal, improve the signal-to-clutter ratio and contrast ratio,and have good performance in complicated background suppression.
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