基于beamlet变换的图像线性特征提取

Radon变换常用于图像直线特征的提取，但是在检测线段时，Radon变换无法提供线段的端点以及长度信息，在提取被噪声严重污染的图像中的线性特征时存在巨大困难。beamlet是一个多尺度的在一定范围内的位置、方向和尺度下二进组织的线段库，具有分级、多尺度的特性。beamlet变换是近年来蓬勃发展的多尺度几何分析理沦的一种，特别适合于提取图像中的线性特征。该文描述了一个多尺度图像分析的框架，其中线段起到的作用类似点在小波中起的作用。论文研究了beamlet变换的实现方法，并将beamlet变换应用于被噪声严重污染的图像中线性特征的提取。实验证明该方法具有很好的效果。

Line Detection in SAR Imagery Based on Beamlet Transform

Radon transform can be used for detecting linear features in an image. However, there is enormous difficulty while detecting line in the images because Radon transform can not provide information about the positions of endpoints and the length. These problems become more serious when the image is polluted seriously by the noise. Beamlets are a special dynamically organized collection of line segments, exhibiting a range of lengths, positions and orientations. Beamlet transform is one kind of multiscale geometric analysis (MGA) developed fast in recent years, especially suited to the line detection in the images. This paper describes a framework for multiscale image analysis in which line segments play a role analogous to the role played by points in wavelet analysis. Implementation of beamlet transform has been studied in this paper and it is applied in line detection in the images polluted seriously by the noise. Experimental results show the power of this approach.