文献类型：会议论文

英文题名：Nonparametric Bayesian Tensor Dictionary Learning for Hyperspectral Image Denoising

作者：Lai, Jinzhi[1]; Jiang, Jing[1]; Jiang, Yanjun[2]; Zhang, Xuesong[1]

第一作者：Lai, Jinzhi

机构：[1] School of Computer Science National Pilot Software Engineering School, Beijing University of Posts and Telecommunications, China; [2] Department of Communication Engineering, Beijing Union University, China

第一机构：School of Computer Science National Pilot Software Engineering School, Beijing University of Posts and Telecommunications, China

会议论文集：Proceedings of the 4th International Conference on Machine Learning and Machine Intelligence, MLMI 2021

会议日期：September 17, 2021 - September 19, 2021

会议地点：Virtual, Online, China

语种：英文

外文关键词：Image denoising - Learning systems - Least squares approximations - Numerical methods - Tensors

摘要：In this paper, we present a nonparametric Bayesian dictionary learning method for hyperspectral image (HSI) denoising, which exploits two intrinsic properties of HSIs: the spectral correlation and the spatial nonlocal similarity. We first extract full band patches from the contaminated HSI and then cluster them into groups. Then we represent each group as a fourth order tensor so that the spatial-spectral-nonlocal features can be preserved via Tucker decomposition. As tensor rank estimation is still an open problem, we propose a hierarchical nonparametric Bayesian optimization scheme for the factor matrices inference, in which Beta-Bernoulli process priors are exerted on the core tensor to allow for the datadriven rank determination over different tensors. Furthermore, we optimize the tensor decomposition result via the Nonnegative least square method (NNLS) to constrain the positivity of dictionary atoms. Numerical experimental results verify the efficacy of our method compared with state-of-the-art HSI denoising methods. ? 2021 ACM.