期刊论文

IMAGE DENOISING ALGORITHM BASED ON NORMAL-GAMMA CONJUGATE PRIOR OPTIMIZED FACTOR ANALYSIS

中文

计算机应用与软件

1000-386X

2023

40

12

237-242

10.3969/j.issn.1000-386x.2023.12.035

GDYJSKT19-05:广东省高职高专云计算与大数据专业委员会课题

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采用传统因子分析(Fact Analysis,FA)模型进行噪声抑制时存在因子个数难以确定、噪声抑制后图像质量下降等问题,基于贝叶斯决策理论提出一种Normal-Gamma 共轭先验优化FA 模型的图像去噪算法,利用Normal-Gamma分布对FA加载因子和隐变量的概率分布建模,一方面对不适定噪声抑制问题正则化,另一方面提高模型的稀疏性,增加参数估计稳定性,采用变分贝叶斯期望最大(Variational Bayesian Expectation Maximum,VBEM)算法对模型求解,自动确定因子个数的同时提升噪声抑制性能.基于标准图像数据集的实验结果表明,所提算法在实现噪声抑制的同时较好地保留了图像的边缘和纹理等细节信息,并且能够明显提升低信...

The number of factors is difficult to determine when factor analysis(FA)model is used for noise suppression,and the image quality degrades after noise suppression.Aimed at these problems,based on Bayesian decision theory,an image denoising algorithm based on normal-Gamma conjugate prior optimized FA model is proposed.The normal-Gamma distribution was used to model the probability distribution of FA loading factors and hidden variables.The ill posed noise suppression problem was regularized and the stability of parameter estimation was increased to improve the sparsity of the model.The variable...