联合纹理提取和边缘检测的新方法

结合稀疏表示和半二次规整化方法，提出了一种联合纹理特征提取和边缘检测的新算法。该算法是基于稀疏表示的形态学成分分解方法的直接推广。其基本思想是用两个适合的字典：一个用来描述纹理部分--对偶树复小波变换，另一个用来描述结构部分--第二代曲线波变换，得到了一种新的分解模型。接着运用半二次规整化方法推广这个分解模型，提出了一种联合纹理特征提取和边缘检测的变分模型。数值计算的结果表明，新模型对图像的结构〖CD*2〗纹理分解，以及边缘的提取都有较好的效果。

New model combining texture extraction with edge detection

A novel algorithm for extracting texture and detecting edge is presented based on sparse representations and half-quadratic regularization, which is a direct extension of the recently developed sparse- representation-based image decomposition method called morphological component analysis (MCA). The basic idea presented in this method is the use of two appropriate dictionaries, one for the representation of texture parts--the dual tree complex wavelet transform and the other for the cartoon parts--the second generation of curvelet transform. It results in a new decomposition model. Then by using the method of half-quadratic regularization, a new method combining texture extraction with edge detection is derived based on the new decomposition model. Finally, numerical experiments show that the new method can decompose a given image into a cartoon and an oscillatory component better while detecting contour well and truly.