Bayesian method with sparsity enforcing prior of dual-tree complex wavelet transform coefficients for X-ray CT image reconstruction

摘要

In this paper, a Bayesian method with a hierarchical sparsity enforcing prior model for Dual-Tree Complex Wavelet Transform (DT-CWT) coefficients is proposed. This model is used for X-ray Computed Tomography (CT) image reconstruction. A generalized Student-t distributed prior model is used to enforce the sparse structure of the DT-CWT coefficient of the image. The joint Maximum A Posterior algorithm (JMAP) is used in this Bayesian context. Comparisons with the conventional and other state-of-the-art methods are presented, showing that the proposed method gives more accurate and robust reconstruction results while the dataset is insufficient.