Results 41 to 50 of about 3,137 (156)

Gap-Filling of NDVI Satellite Data Using Tucker Decomposition: Exploiting Spatio-Temporal Patterns

Remote Sensing, 2021
Remote sensing satellite images in the optical domain often contain missing or misleading data due to overcast conditions or sensor malfunctioning, concealing potentially important information. In this paper, we apply expectation maximization (EM) Tucker
Andri Freyr Þórðarson, Andreas Baum, Mónica García, Sergio M. Vicente-Serrano, Anders Stockmarr   +4 more
doaj   +1 more source

Parallel Randomized Tucker Decomposition Algorithms

SIAM Journal on Scientific Computing
The Tucker tensor decomposition is a natural extension of the singular value decomposition (SVD) to multiway data. We propose to accelerate Tucker tensor decomposition algorithms by using randomization and parallelization. We present two algorithms that scale to large data and many processors, significantly reduce both computation and communication ...
Rachel Minster, Zitong Li, Grey Ballard
openaire   +3 more sources

Tensor Regression Using Low-Rank and Sparse Tucker Decompositions

SIAM Journal on Mathematics of Data Science, 2020
This paper studies a tensor-structured linear regression model with a scalar response variable and tensor-structured predictors, such that the regression parameters form a tensor of order $d$ (i.e., a $d$-fold multiway array) in $\mathbb{R}^{n_1 \times n_2 \times \cdots \times n_d}$.
Talal Ahmed, Haroon Raja, Waheed U. Bajwa   +2 more
openaire   +3 more sources

Hyperspectral Compressive Image Reconstruction With Deep Tucker Decomposition and Spatial–Spectral Learning Network

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023
Hyperspectral compressive imaging has taken advantage of compressive sensing theory to capture spectral information of the dynamic world in recent decades of years, where an optical encoder is employed to compress high dimensional signals into a single 2-
Hao Xiang, Baozhu Li, Le Sun, Yuhui Zheng, Zebin Wu, Jianwei Zhang, Byeungwoo Jeon   +6 more
doaj   +1 more source

High Dynamic Range Image Watermarking Based on Tucker Decomposition

IEEE Access, 2019
High dynamic range (HDR) imaging technique has received much attentions in the recent years for its abundant details and wide dynamic range of luminance.
Mei Yu, Yang Wang, Gangyi Jiang, Yongqiang Bai, Ting Luo   +4 more
doaj   +1 more source

Hyperspectral Image Denoising via Low-Rank Tucker Decomposition with Subspace Implicit Neural Representation

Remote Sensing
Hyperspectral image (HSI) denoising is an important preprocessing step for downstream applications. Fully characterizing the spatial-spectral priors of HSI is crucial for denoising tasks.
Cheng Cheng, Dezhi Sun, Yaoyuan Yang, Zhoucheng Guo, Jiangjun Peng   +4 more
doaj   +1 more source

Hyperspectral Image Superresolution Using Unidirectional Total Variation With Tucker Decomposition

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020
The hyperspectral image superresolution (HSI-SR) problem aims to improve the spatial quality of a low spatial resolution HSI by fusing the LR-HSI with the corresponding high spatial resolution multispectral image.
Ting Xu, Ting-Zhu Huang, Liang-Jian Deng, Xi-Le Zhao, Jie Huang   +4 more
doaj   +1 more source

Harmonic Retrieval with $L_1$-Tucker Tensor Decomposition

, 2021
Harmonic retrieval (HR) has a wide range of applications in the scenes where signals are modelled as a summation of sinusoids. Past works have developed a number of approaches to recover the original signals. Most of them rely on classical singular value decomposition, which are vulnerable to unexpected outliers.
Luan, Zhenting, Ming, Zhenyu, Wu, Yuchi, Han, Wei, Chen, Xiang, Bai, Bo, Zhang, Liping   +6 more
openaire   +2 more sources

Hypergraph regularized nonnegative triple decomposition for multiway data analysis

Scientific Reports
Tucker decomposition is widely used for image representation, data reconstruction, and machine learning tasks, but the calculation cost for updating the Tucker core is high.
Qingshui Liao, Qilong Liu, Fatimah Abdul Razak   +2 more
doaj   +1 more source

A tensor compression algorithm using Tucker decomposition and dictionary dimensionality reduction

International Journal of Distributed Sensor Networks, 2020
Tensor compression algorithms play an important role in the processing of multidimensional signals. In previous work, tensor data structures are usually destroyed by vectorization operations, resulting in information loss and new noise. To this end, this
Chenquan Gan, Junwei Mao, Zufan Zhang, Qingyi Zhu   +3 more
doaj   +1 more source