复轮廓波包的构造及其图像去噪应用

一般的轮廓波变换只对信号的低频部分进行分解,却忽略了信号的高频部分,因而丢失了丰富的细节和纹理信息,为了克服这种缺陷,本文利用解析的双树复小波包变换和非抽样方向滤波器组,构造了复轮廓波包变换,并提出一种基于相邻系数阈值分类的复轮廓波包图像去噪算法.新的变换除了具有多分辨率、局部性、多方向性和各向异性的特点外,还具有平移不变性和更丰富的方向分量.仿真试验结果表明,构造的复轮廓波包变换能够有效地抑制伪Gibbs现象,并且保护更多的边缘和纹理等细节,其PSNR值和视觉质量均优于一般的去噪方法.

Construction of Complex Contourlet Packet Transform and Its Application to Image Denoising

Considering the normal contourlet transform only decomposed the low frequency coefficients of the signals,and ignored the high frequency coefficients,a novel complex contourlet packet transform is constructed by combining the analytic dual-tree complex wavelet packet transform and nonsubsampled directional filter banks. Then a complex contourlet packet image denoising algorithm based on neighbouring thresholding classification is proposed. The new transform has good characteristics of multiresolution,localization,directionality and anisotropy,as well as translation invariance. Furthermore,it has more abundant direction components. The experimental result shows that the complex contourlet packet transform can restrain Gibbs-like artificial around edges in the course of denoising,and preserve more details and textures of the images efficiently. The PSNR and the visual quality of this algorithm are also superior to the traditional methods.