Nonparametric Bayesian dictionary learning algorithm based on structural similarity

被引：0
作者：
Dong D. [1 ]
Rui G. [1 ]
Tian W. [1 ]
Kang J. [1 ]
Liu G. [1 ]
机构：
[1] Signal and Information Processing Key Laboratory in Shandong, Navy Aviation University, Yantai
来源：
Tongxin Xuebao/Journal on Communications | 2019年 / 40卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Compressed sensing; Denoising; Dictionary learning; Nonparametric Bayesian; Structural similarity;
D O I：
10.11959/j.issn.1000-436x.2019015
中图分类号：
学科分类号：
摘要：
Though nonparametric Bayesian methods possesses significant superiority with respect to traditional comprehensive dictionary learning methods, there is room for improvement of this method as it needs more consideration over the structural similarity and variability of images. To solve this problem, a nonparametric Bayesian dictionary learning algorithm based on structural similarity was proposed. The algorithm improved the structural representing ability of dictionaries by clustering images according to their non-local structural similarity and introducing block structure into sparse representing of images. Denoising and compressed sensing experiments showed that the proposed algorithm performs better than several current popular unsupervised dictionary learning algorithms. © 2019, Editorial Board of Journal on Communications. All right reserved.
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收藏
页码：43 / 50
页数：7
相关论文
共 19 条
  • [1] Fedorov I., Rao B.D., Nguyen T.Q., Multimodal sparse Bayesian dictionary learning applied to multimodal data classification, IEEE International Conference on Acoustics, pp. 2237-2241, (2017)
  • [2] Huang Y., Paisley J., Lin Q., Et al., Bayesian nonparametric dictionary learning for compressed sensing MRI, IEEE Transactions on Image Processing, 23, 12, pp. 5007-5019, (2014)
  • [3] Li S., Tao X., Lu J., Variational Bayesian inference for nonparametric signal compressive sensing on structured manifolds, IEEE International Conference on Communications, pp. 1-6, (2017)
  • [4] Li Z., Huang H., Misra S., Compressed sensing via dictionary learning and approximate message passing for multimedia Internet of things, IEEE Internet of Things Journal, 4, 2, pp. 505-512, (2017)
  • [5] Huang Y., Paisley J., Lin Q., Et al., Bayesian nonparametric dictionary learning for compressed sensing MRI, IEEE Transactions on Image Processing, 23, 12, pp. 5007-5019, (2014)
  • [6] Hong P.D., Chainais P., A Bayesian non-parametric approach to learn dictionaries with adapted numbers of atoms, IEEE International Workshop on Machine Learning for Signal Processing, pp. 1-6, (2015)
  • [7] Zhou M., Chen H., Paisley J., Et al., Nonparametric Bayesian dictionary learning for analysis of noisy and incomplete images, IEEE Transactions on Image Processing, 21, 1, pp. 130-144, (2012)
  • [8] Liu Z., Yu L., Zhang M., Et al., Nonlocal structured nonparametric Bayesian dictionary learning for image denoising, IEEE International Conference on Signal Processing, pp. 144-148, (2017)
  • [9] Zhou M., Yang H., Sapiro G., Et al., Dependent hierarchical beta process for image interpolation and denoising, Aistats, 15, pp. 883-891, (2012)
  • [10] Paisley J., Zhou M., Sapiro G., Et al., Nonparametric image interpolation and dictionary learning using spatially-dependent dirichlet and beta process priors, IEEE International Conference on Image Processing, pp. 1869-1872, (2010)
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