Bayesian deep matrix factorization network for multiple images denoising.

Clicks: 266
ID: 85404
2020
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Ranked #15 of 55 articles by views in neural networks : the official journal of the international neural network society

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Abstract
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.
Reference Key
xu2020bayesianneural Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Xu, Shuang;Zhang, Chunxia;Zhang, Jiangshe;
Journal neural networks : the official journal of the international neural network society
Year 2020
DOI
S0893-6080(19)30423-X
URL
Keywords
matrix factorization bayesian neural networks variational bayes

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