.png){kind=icon}
.png){kind=icon}
Return
Nonnegative and Nonlocal Sparse Tensor Factorization-Based Hyperspectral Image Super-Resolution
.png){kind=icon}
PRE
AI
DOI：10.1109/TGRS.2020.2987530.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Hyperspectral image (HSI) super-resolution refers to enhancing the spatial resolution of a 3-D image with many spectral bands (slices). It is a seriously ill-posed problem when the low-resolution (LR) HSI is the only input. It is better solved by fusing the LR HSI with a high-resolution (HR) multispectral image (MSI) for a 3-D image with both high spectral and spatial resolution. In this article, we propose a novel nonnegative and nonlocal 4-D tensor dictionary learning-based HSI super-resolution model using group-block sparsity. By grouping similar 3-D image cubes into clusters and then conduct super-resolution cluster by cluster using 4-D tensor structure, we not only preserve the structure but also achieve sparsity within the cluster due to the collection of similar cubes. We use 4-D tensor Tucker decomposition and impose nonnegative constraints on the dictionaries and group-block sparsity. Numerous experiments demonstrate that the proposed model outperforms many state-of-the-art HSI super-resolution methods.
Keywords:
Tensors
Dictionaries
Sparse matrices
Noise reduction
Spatial resolution
Correlation
Hyperspectral imaging (HSI)
nonlocal sparse tensor factorization (NLSTF)
nonnegative tensor dictionary learning
super-resolution
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
8.6
Papers:
2.1W
Citations:
10.7W