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.

中文翻译：

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用于多图像降噪的贝叶斯深度矩阵分解网络。

本文旨在为多图像降噪提出一个鲁棒且快速的低秩矩阵分解模型。为此，提出了一种新颖的模型贝叶斯深度矩阵分解网络（BDMF），其中设计了一个深度神经网络（DNN）来建模低秩分量，并通过随机梯度变分贝叶斯算法对该模型进行了优化。与现有的最新模型相比，凭借深度学习和贝叶斯建模，BDMF在合成实验和实际任务（包括阴影去除和高光谱图像降噪）方面取得了显着改进。