Bayesian deep matrix factorization network for multiple images denoising.
Clicks: 180
ID: 85404
2020
Article Quality & Performance Metrics
Overall Quality
Improving Quality
0.0
/100
Combines engagement data with AI-assessed academic quality
Reader Engagement
Emerging Content
6.6
/100
22 views
22 readers
Trending
AI Quality Assessment
Not analyzed
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 |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.