| 含Laplace-Gauss型混合噪声图像二阶正则化重建方法（英文） |
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| 引用本文： | 孔令海,孔令波,许海波,贾清刚. 含Laplace-Gauss型混合噪声图像二阶正则化重建方法（英文）[J]. 计算物理, 2019, 0(3): 280-290 |
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| 作者姓名： | 孔令海 孔令波 许海波 贾清刚 |
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| 作者单位： | 北京应用物理与计算数学研究所;北京交通大学软件工程学院 |
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| 基金项目： | Supported by the National Science Foundation of China(11571003) |
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| 摘 要： | 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.
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| 关 键 词： | 客体重建 自适应软收缩 四阶偏微分方程 交替方向增广拉格朗日方法 |
A Higher Order Regularization Approach for Object Reconstruction with Mixed Laplace-Gaussian Likelihood |
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| KONG Linghai,KONG Lingbo,XU Haibo,JIA Qinggang. A Higher Order Regularization Approach for Object Reconstruction with Mixed Laplace-Gaussian Likelihood[J]. Chinese Journal of Computational Physics, 2019, 0(3): 280-290 |
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| Authors: | KONG Linghai KONG Lingbo XU Haibo JIA Qinggang |
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| Affiliation: | (Institute of Applied Physics and Computational Mathematics,Beijing 100094,China;School of Software Engineering, Beijing Jiaotong University,Beijing 100044,China) |
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| Abstract: | 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. |
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| Keywords: | object reconstruction adaptive soft-shrinking fourth-order partial differential equation ADAL |
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