Results 31 to 40 of about 1,781 (261)

LRTCFPan: Low-Rank Tensor Completion Based Framework for Pansharpening

IEEE Transactions on Image Processing, 2023
Pansharpening refers to the fusion of a low spatial-resolution multispectral image with a high spatial-resolution panchromatic image. In this paper, we propose a novel low-rank tensor completion (LRTC)-based framework with some regularizers for multispectral image pansharpening, called LRTCFPan.
Wu, Zhong-Cheng, Huang, Ting-Zhu, Deng, Liang-Jian, Huang, Jie, Chanussot, Jocelyn, Vivone, Gemine   +5 more
openaire   +5 more sources

Low-Rank Tensor Completion and Total Variation Minimization for Color Image Inpainting

IEEE Access, 2020
Low-rank (LR) and total variation (TV) are two most frequent priors that occur in image processing problems, and they have sparked a tremendous amount of researches, particularly for moving from scalar to vector, matrix or even high-order based functions.
Mengjie Qin, Zhuorong Li, Shengyong Chen, Qiu Guan, Jianwei Zheng   +4 more
doaj   +1 more source

Nonnegative Tensor Completion via Low-Rank Tucker Decomposition: Model and Algorithm

IEEE Access, 2019
We consider the problem of low-rank tensor decomposition of incomplete tensors that has applications in many data analysis problems, such as recommender systems, signal processing, machine learning, and image inpainting.
Bilian Chen, Ting Sun, Zhehao Zhou, Yifeng Zeng, Langcai Cao   +4 more
doaj   +1 more source

Orthogonal random projection for tensor completion

IET Computer Vision, 2020
The low‐rank tensor completion problem, which aims to recover the missing data from partially observable data. However, most of the existing tensor completion algorithms based on Tucker decomposition cannot avoid using singular value decomposition (SVD ...
Yali Feng, Guoxu Zhou
doaj   +1 more source

A New Model for Tensor Completion: Smooth Convolutional Tensor Factorization

IEEE Access, 2023
Tensor completion is the problem of filling-in missing parts of multidimensional data using the values of the reference elements. Recently, Multiway Delay-embedding Transform (MDT), which considers a low-dimensional space in a delay-embedded space with ...
Hiromu Takayama, Tatsuya Yokota
doaj   +1 more source

Noisy Low-Tubal-Rank Tensor Completion Through Iterative Singular Tube Thresholding

IEEE Access, 2018
In many applications, data organized in tensor form contains noise and missing entries. In this paper, the goal is to complete a tensor from its partial noisy observations.
Andong Wang, Dongxu Wei, Bo Wang, Zhong Jin   +3 more
doaj   +1 more source

Efficient tensor completion: Low-rank tensor train

, 2016
11 pages, 9 ...
Phien, Ho N., Tuan, Hoang D., Bengua, Johann A., Do, Minh N.   +3 more
openaire   +2 more sources

Low-Rank Approximation and Completion of Positive Tensors [PDF]

SIAM Journal on Matrix Analysis and Applications, 2016
Unlike the matrix case, computing low-rank approximations of tensors is NP-hard and numerically ill-posed in general. Even the best rank-1 approximation of a tensor is NP-hard. In this paper, we use convex optimization to develop polynomial-time algorithms for low-rank approximation and completion of positive tensors.
openaire   +3 more sources

Higher-dimension Tensor Completion via Low-rank Tensor Ring Decomposition [PDF]

2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2018
APSIPA2018 conference paper.
Yuan, Longhao, Cao, Jianting, Wu, Qiang, Zhao, Qibin   +3 more
openaire   +3 more sources

Accelerated Low-Rank Tensor Completion via Projected Tensor Block Coordinate Descent

IEEE Access
The low-rank tensor completion problem aims to find a low-rank approximation of a tensor by filling in missing entries from partially observed entries to enhance the accuracy of the tensor data analysis.
Geunseop Lee
doaj   +1 more source