9 citations · 10 across the 4 of their papers we have counts for
4 papers
Multi-modal and frequency-weighted tensor nuclear norm for hyperspectral image denoising
Xiaozhen Xie, Sheng Liu
Low-rankness is important in the hyperspectral image (HSI) denoising tasks. The tensor nuclear norm (TNN), defined based on the tensor singular value decomposition, is a state-of-t…
Hyperspectral Image Denoising via Multi-modal and Double-weighted Tensor Nuclear Norm
Sheng Liu, Xiaozhen Xie, Wenfeng Kong
Hyperspectral images (HSIs) usually suffer from different types of pollution. This severely reduces the quality of HSIs and limits the accuracy of subsequent processing tasks. HSI…
Multi-mode Core Tensor Factorization based Low-Rankness and Its Applications to Tensor Completion
Haijin Zeng
Low-rank tensor completion has been widely used in computer vision and machine learning. This paper develops a novel multi-modal core tensor factorization (MCTF) method combined wi…
Machine Discovery of Partial Differential Equations from Spatiotemporal Data
Ye Yuan, Junlin Li, Liang Li +9
The study presents a general framework for discovering underlying Partial Differential Equations (PDEs) using measured spatiotemporal data. The method, called Sparse Spatiotemporal…