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An improved infrared dim and small target detection algorithm based on the contrast mechanism of human visual system

机译:一种基于人眼视觉系统对比度机制的红外弱小目标检测算法

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摘要

To achieve higher detection rate and lower false alarm rate in dim and small target detection, this paper proposed an improved algorithm based on the contrast mechanism of human visual system (HVS) for infrared small target detection in an image with complicated background. According to the contrast mechanism of HVS, Laplacian of Gaussian (LoG) filter is exploited to deal with the input image, which can not only suppress the background noise and clutter but also enhances the target intensity significantly. As a result it increases the contrast ratio between target and background. To further eliminate residual clutter, we process the filtered image with morphological method in all directions. True target is finally obtained by applying local thresholding segmentation to the pre-processed image. Experimental results demonstrate its superior and reliable detection performance by high detection rate and low false alarm rate.
机译:为了在昏暗和小目标检测中达到更高的检测率和更低的误报率,本文提出了一种基于人类视觉系统(HVS)对比机制的改进算法,用于背景复杂图像中的红外小目标检测。根据HVS的对比度机制,利用高斯拉普拉斯算子(LoG)对输入图像进行处理,不仅可以抑制背景噪声和杂波,而且可以显着提高目标强度。结果,它增加了目标和背景之间的对比度。为了进一步消除残留的杂波,我们使用形态学方法在所有方向上对滤波后的图像进行处理。通过将局部阈值分割应用于预处理图像,最终获得了真实目标。实验结果证明了其较高的检测率和较低的误报率,具有优越而可靠的检测性能。

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