期刊
NEURAL NETWORKS
卷 123, 期 -, 页码 420-428出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2019.12.023
关键词
Matrix factorization; Bayesian neural networks; Variational Bayes
资金
- Fundamental Research Funds for the Central Universities, China [xzy022019059]
- National Key Research and Development Program of China [2018YFC0809001]
- National Natural Science Foundation of China [61976174]
This paper aims at proposing a robust and fast low rank matrix factorization model for multiple images denoising. To this end, a novel model, Bayesian deep matrix factorization network (BDMF), is presented, where a deep neural network (DNN) is designed to model the low rank components and the model is optimized via stochastic gradient variational Bayes. By the virtue of deep learning and Bayesian modeling, BDMF makes significant improvement on synthetic experiments and real-world tasks (including shadow removal and hyperspectral image denoising), compared with existing state-of-the-art models. (C) 2019 Elsevier Ltd. All rights reserved.
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