A Wavelet Shrinkage Approach to Tomographic Image Reconstruction
提出一种基于二维小波基的断层图像重建方法，利用小波-模糊分解框架推导小波系数，结合滤波反投影算法和小波收缩去噪，实现正则化重建，并与传统方法比较。
Abstract A method is proposed for reconstructing images from tomographic data with respect to a two-dimensional wavelet basis. The Wavelet-vaguelette decomposition (WVD) is used as a framework within which expressions for the necessary wavelet coefficients may be derived. These coefficients are calculated using a version of the filtered back-projection algorithm as a computational tool, in a multiresolution fashion. The necessary filters are defined in terms of the underlying wavelets. Denoising is accomplished through an adaptation of the wavelet shrinkage (WS) approach of Donoho et al. and amounts to a form of regularization. Combining these two steps yields the proposed WVD/WS reconstruction algorithm, which is compared to the traditional filtered backprojection method in a small study.