Bayesian deep matrix factorization network for multiple images denoising

Shuang Xu, Chunxia Zhang, Jiangshe Zhang

科研成果: 期刊稿件文章同行评审

22 引用 (Scopus)

摘要

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.

源语言英语
页(从-至)420-428
页数9
期刊Neural Networks
123
DOI
出版状态已出版 - 3月 2020
已对外发布

指纹

探究 'Bayesian deep matrix factorization network for multiple images denoising' 的科研主题。它们共同构成独一无二的指纹。

引用此