Apr 29, 2024 · Hyperspectral image super-resolution aims to enhance spatial resolution and reduce redundant interference, but it is often an ill-posed inverse ...
In this article, we propose a novel low-rank tensor model by incorporating the manifold regularization for hyperspectral super-resolution image reconstruction.
A novel tensor-based super-resolution model that incorporates multimode low-rank and graph-based manifold regularization (M-LGMR) for performance ...
Abstract: Hyperspectral imagery typically exhibits high spectral resolution but low spatial resolution due to limitations in imaging sensors.
Shuai Huo's 4 research works with 12 citations, including: Multiscale reweighted smoothing regularization in curvelet domain for hyperspectral image ...
We propose a fast low-tensor multi-rank (FLTMR) regularization method for super-resolution processing of hyperspectral images, which improves the coefficient ...
Missing: Multimode Relaxation
In this paper, the hyperspectral image super-resolution problem is transformed into a joint regularization optimization problem based on tensor decomposition ...
Missing: Relaxation | Show results with:Relaxation
We propose a novel subspace-based low tensor multi-rank regularization method for the fusion, which fully exploits the spectral correlations and non-local ...
Missing: Relaxation | Show results with:Relaxation
Li et al. Super-resolution reconstruction of hyperspectral images via low rank tensor modeling and total variation regularization, IGARSS2016, S. He et al. [PDF].
Missing: Multimode Relaxation
Multimode Low-Rank Relaxation and Manifold Regularization for Hyperspectral Image Super-Resolution ... Representation for Hyperspectral Image Super-Resolution.