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Dim small target detection in single frame complex background

机译:单帧复杂背景下的弱小目标检测

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High precision detection of small targets in complex background is a challenging task, which has not been well resolved. In this paper, the improve sparse representation(ISR) algorithm is proposed based on the characteristics of passive millimeter wave imaging as well as the difference of the priori characteristics between the small target and background clutter. Firstly, the algorithm constructs a over-complete dictionary based on the content of the image itself, and then improves the original sparse representation method to complete precise classification of target and background dictionaries. After background suppression and target enhancement, we can easily extract the target. The millimeter wave images of different scenes are detected and the results show that compared with some other mainstream algorithms, the ISR algorithm has lower false alarm rate, higher detection accuracy and stronger robustness.
机译:在复杂背景下对小目标进行高精度检测是一项艰巨的任务,尚未得到很好的解决。基于无源毫米波成像的特点,以及小目标和背景杂波的先验特征的差异,提出了一种改进的稀疏表示算法。该算法首先基于图像本身的内容构造了一个超完备的字典,然后对原始的稀疏表示方法进行了改进,以完成对目标字典和背景字典的精确分类。经过背景抑制和目标增强后,我们可以轻松提取目标。结果表明,与其他主流算法相比,ISR算法具有较低的误报率,较高的检测精度和较强的鲁棒性。

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