1. Hyperspectral Image Superresolution Using Global Gradient Sparse and Nonlocal Low-Rank Tensor Decomposition With Hyper-Laplacian Prior
- Author
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Yidong Peng, Xiaobo Luo, Jiao Du, and Weisheng Li
- Subjects
Atmospheric Science ,hyperspectral image ,Rank (linear algebra) ,Computer science ,Multispectral image ,hyper-Laplacian ,Geophysics. Cosmic physics ,0211 other engineering and technologies ,02 engineering and technology ,Global gradient sparse ,0202 electrical engineering, electronic engineering, information engineering ,Tensor ,Computers in Earth Sciences ,TC1501-1800 ,021101 geological & geomatics engineering ,Sparse matrix ,QC801-809 ,Hyperspectral imaging ,Total variation denoising ,Ocean engineering ,total variation ,nonlocal low-rank ,Physics::Accelerator Physics ,020201 artificial intelligence & image processing ,Algorithm ,Laplace operator ,superresolution ,Tucker decomposition - Abstract
This article presents a novel global gradient sparse and nonlocal low-rank tensor decomposition model with a hyper-Laplacian prior for hyperspectral image (HSI) superresolution to produce a high-resolution HSI (HR-HSI) by fusing a low-resolution HSI (LR-HSI) with an HR multispectral image (HR-MSI). Inspired by the investigated hyper-Laplacian distribution of the gradients of the difference images between the upsampled LR-HSI and latent HR-HSI, we formulate the relationship between these two datasets as a $\ell _{p}$ $(0 < p < 1)$-norm term to enforce spectral preservation. Then, the relationship between the HR-MSI and latent HR-HSI is built using a tensor-based fidelity term to recover the spatial details. To effectively capture the high spatio-spectral-nonlocal similarities of the latent HR-HSI, we design a novel nonlocal low-rank Tucker decomposition to model the 3-D regular tensors constructed from the grouped nonlocal similar HR-HSI cubes. The global spatial-spectral total variation regularization is then adopted to ensure the global spatial piecewise smoothness and spectral consistency of the reconstructed HR-HSI from nonlocal low-rank cubes. Finally, an alternating direction method of multipliers-based algorithm is designed to efficiently solve the optimization problem. Experiments on both the synthetic and real datasets collected by different sensors show the effectiveness of the proposed method, from visual and quantitative assessments.
- Published
- 2021