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Detection of infrared dim and small targets based on saliency and grayscale morphological reconstruction

机译:基于显着性和灰度形态重构的红外弱小目标检测

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Based on saliency and grayscale morphological reconstruction, a new detection algorithm for infrared dim and small targets is proposed in this paper. The saliency of the original image to obtain the region of interest (ROI) is analyzed, then the spatial domain characteristic of dim and small targets is introduced into the marker image. Grayscale morphological reconstruction is based on the marker image and the mask image (the original image). Because saliency efficiently concentrates the gradient difference of the targets, detection probability is improved also with little false alarms. As the role of recognition, spatial domain characteristic of target reduces false alarm probability with the same detection probability, and background can be well estimated by grayscale morphological reconstruction, after subtraction, dim and small targets are detected. Experiments of real data prove the better detection performance, especially higher signal-to-noise ratio (SNR).
机译:基于显着性和灰度形态重构,提出了一种新的红外弱小目标检测算法。分析原始图像的显着性以获得感兴趣区域(ROI),然后将昏暗目标和小目标的空间域特征引入标记图像。灰度形态重建基于标记图像和蒙版图像(原始图像)。因为显着性有效地集中了目标的梯度差异,所以在几乎没有虚警的情况下也提高了检测概率。作为识别的作用,目标的空间域特征以相同的检测概率降低了虚警概率,并且通过减去减法,暗淡和小的目标,可以通过灰度形态重构很好地估计背景。真实数据的实验证明了更好的检测性能,尤其是更高的信噪比(SNR)。

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