• 图形图像 •

面向图像复原的分层贝叶斯局部高斯混合模型

1. 1. 国家数字交换系统工程技术研究中心，郑州 450000
2. 河南财经政法大学 计算机与信息工程学院，郑州 450000
• 出版日期:2020-02-01 发布日期:2020-02-16

Hierarchical Bayesian Local Gaussian Mixture Model for Image Restoration

ZHANG Mohua, PENG Jianhua

1. 1. National Digital Switching System Engineering & Technological Research Center, Zhengzhou 450000, China
2. College of Computer & Information Engineering, Henan University of Economics and Law, Zhengzhou 450000, China
• Online:2020-02-01 Published:2020-02-16

Abstract:

In recent years, Bayesian approach using Gaussian model as a patch prior has achieved great success in image denoising. However, this approach is not stable in solving inverse problems beyond denoising. A hierarchical Bayesian-based Gaussian mixture model is proposed to model image patch. Using the prior knowledge of the model parameters, the probability distribution of the mean and covariance matrices are modelled by the Gaussian-Wishart distribution，which makes the patch estimation process more stable. Based on the coherence of neighboring patches, the set of similar patches in the window can be derived by the multivariate Gaussian probability distribution of specific mean and covariance. The similarity is measured by the L2-norm metric, which is accelerated by using the summed square image and fast Fourier transform. The aggregation weights are based on the Gaussian distribution similarity with Mahalanobis distance, which are combined with the Gaussian similarity of the spatial domain on the image. The statistical characteristics of the natural image are better fitted. The experimental results for solving image restoration problem demonstrate the capabilities of the proposed method.