含Laplace-Gauss型混合噪声图像二阶正则化重建方法

假设观测数据含Laplace-Gauss型混合噪声条件下，提出求解数据重建反问题的一种新型一阶二阶混合正则化模型，阐述该模型在断层重建和流体动力学实验定量诊断中的应用.建模过程采用贝叶斯推断理论和期望极大方法，将空间自适应函数引入经典的增广拉格朗日方法得到模型数值算法.所提出的模型及其算法进行图像复原和客体重建实验.结果表明模型算法的可靠性.

A Higher Order Regularization Approach for Object Reconstruction with Mixed Laplace-Gaussian Likelihood

A combined first and second order variational model is proposed for reconstructing images corrupted by mixed Laplace-Gaussian noise. The model is constructed by joint maximum a posteriori estimation and expectation maximization. Numerical algorithm is studied by integrating splitting technique into augmented Lagrangian method with modification, such as introduction of adaptively selective functions for preserving details of original images. An adaptive soft-shrinking formulation is advanced for mixed noise removal, in which an alternating minimization algorithm is established. Numerical experiments show validation in tomography reconstruction and image restoration.