论文标题
在低光条件下保留图像特征匹配性能
Retaining Image Feature Matching Performance Under Low Light Conditions
论文作者
论文摘要
弱光图像中的图像质量差可能导致图像之间的特征匹配数量减少。在本文中，我们研究了弱光环境中特征提取算法的性能。为了找到最佳设置以在弱光图像中保留特征匹配性能，我们研究了特征检测器的特征接受阈值的效果，并在功能检测之前以低光图像增强（LLIE）的形式添加预处理。我们观察到，即使在弱光图像中，使用传统手工制作的特征探测器的功能匹配仍然可以通过降低阈值参数来表现出色。我们还表明，在与正确的特征提取算法配对时，应用低光图像增强（LLIE）算法可以改善功能匹配。
Poor image quality in low light images may result in a reduced number of feature matching between images. In this paper, we investigate the performance of feature extraction algorithms in low light environments. To find an optimal setting to retain feature matching performance in low light images, we look into the effect of changing feature acceptance threshold for feature detector and adding pre-processing in the form of Low Light Image Enhancement (LLIE) prior to feature detection. We observe that even in low light images, feature matching using traditional hand-crafted feature detectors still performs reasonably well by lowering the threshold parameter. We also show that applying Low Light Image Enhancement (LLIE) algorithms can improve feature matching even more when paired with the right feature extraction algorithm.
