针对传统Beamlet变换无结构算法在提取图像线特征时存在的线断裂、重叠、模糊等问题，提出了一种提取复杂图像线特征的改进方法。该方法首先利用小波变换对图像进行预处理，以突显细节特征；接着对预处理后的图像进行Beamlet变换，得到变换系数集合；然后在阈值化时，定义了新的能量统计，在可视化时，制定了新的划线规则，并使两者结合，以确保每个二进方块最多只用一条最优基表征；最后将所有方块中的最优基作为线特征提取出来。实验结果表明，与传统算法相比，在没有明显增加计算量的前提下,该改进方法对线条丰富和边缘复杂的图像的线特征提取，表现出明显的优势。

Traditional linear feature detection methods based on structureless algorithms of Beamlet transform are mostly used to detect simple line segments and curves, while fail to detect complicated edges in natural images. Wavelet transform has great advantages in point feature detection, meaning that it is good at detecting edge and details. In this paper we improve traditional methods with the help of wavelet. Meanwhile, energy function in traditional algorithm is improved and a new drawing linear feature rule is proposed in order to represent a dyadic square with at most one optimal Beamlet. First, image is decomposed into low frequency and high frequencies with wavelet to highlight edge detail feature; second, the edge image’s transform coefficients are obtained by Beamlet transform. Finally the coefficients are dealt with using the improved energy function and linear features are extracted following the new drawing rule. Experimental results show that without costing obvious extra computing time, our proposed method can extract complete and clear linear features in natural images.