Contents 1. Introduction
Edge in an image may be defined as a rapid photometric change in the registered intensity values. From a pixel level perspective, edge can be viewed as the regions of an image where the image values undergo a sharp variation. In discrete 2-D image edge is detected as linear combination of the point's singularities between pixels. Noise is among the most significant obstacles of edge detection. Most of edge detectors only produce points at the positions of edges of images. The popular edge detectors like those of Canny, Sobel, etc. did just that [1]. Nevertheless, the applications of these detectors are limited especially in noisy image. As we know, besides pixels, there still are curves and other patterns in image. To exact curves and patterns, high dimensional singularities processing ability is demanded, which is strikingly similar to physiological peculiarity of human eyes. Beamlet tansform[2] is a new tool of detecting linear feature target in image, but the traditional beamlet transform is absent the ability of detecting edge. So we improve the traditional beamlet transform and present the directional beamlet transform.
