Mixed noise removal based on a novel non-parametric Bayesian sparse outlier model
Mixed noise removal based on a novel non-parametric Bayesian sparse outlier model
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DOI：
10.1016/j.neucom.2015.09.095
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发表时间：
2016-01
期刊：
影响因子：
6
通讯作者：
Peixian Zhuang;Yue Huang;Delu Zeng;Xinghao Ding
中科院分区：
文献类型：
--
作者：
Peixian Zhuang;Yue Huang;Delu Zeng;Xinghao Ding
针对混合噪声去除问题，提出了一种新的非参数贝叶斯稀疏离群点模型。该模型基于稀疏数据和孤立野值的假设，将观测数据分解为理想数据、高斯噪声和野值噪声三个分量。然后对孤立噪声和理想数据的稀疏系数采用尖峰板先验。所提出的方法可以自动推断噪声统计（例如，高斯噪声方差），而不改变模型超参数设置。该方法不需要像其他去噪方法那样使用自适应中值滤波器，对初始化也具有鲁棒性。实验结果表明，该模型在去除混合噪声方面具有较好的客观和主观性能。
We develop a novel non-parametric Bayesian sparse outlier model for the problem of mixed noise removal. Based on the assumptions of sparse data and isolated outliers, the proposed model is considered for decomposing the observed data into three components of ideal data, Gaussian noise and outlier noise. Then the spike-slab prior is employed for outlier noise and sparse coefficients of ideal data. The proposed method can automatically infer noise statistics (e.g., Gaussian noise variance) from the training data without changing model hyper-parameter settings. It is also robust to initialization without using adaptive median filter as in other denoising methods. Experimental results demonstrate proposed model can achieve better objective and subjective performances on mixed noise removal than other state-of-the-art methods.