Nonlocal Patch Tensor Sparse Representation for Hyperspectral Image Super-Resolution

This paper presents a hypserspectral image (HSI) super-resolution method, which fuses a low-resolution HSI (LR-HSI) with a high-resolution multispectral image (HR-MSI) to get high-resolution HSI (HR-HSI). The proposed method first extracts the nonlocal similar patches to form a nonlocal patch tensor...

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Bibliographic Details
Published in:IEEE transactions on image processing 2019-06, Vol.28 (6), p.3034-3047
Main Authors: Yang Xu, Zebin Wu, Chanussot, Jocelyn, Zhihui Wei
Format: Article
Language:English
Subjects:
Dictionaries
Engineering Sciences
Fuses
High resolution
Hyperspectral image
Hyperspectral imaging
Image resolution
Mathematical analysis
nonlocal patch tensor
Optimization
Representations
Signal and Image processing
Signal resolution
Sparse matrices
Spatial resolution
super-resolution
tensor dictionary learning
tensor sparse coding
Tensors
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Summary:This paper presents a hypserspectral image (HSI) super-resolution method, which fuses a low-resolution HSI (LR-HSI) with a high-resolution multispectral image (HR-MSI) to get high-resolution HSI (HR-HSI). The proposed method first extracts the nonlocal similar patches to form a nonlocal patch tensor (NPT). A novel tensor-tensor product (t - product)-based tensor sparse representation is proposed to model the extracted NPTs. Through the tensor sparse representation, both the spectral and spatial similarities between the nonlocal similar patches are well preserved. Then, the relationship between the HR-HSI and the LR-HSI is built using t - product, which allows us to design a unified objective function to incorporate the nonlocal similarity, tensor dictionary learning, and tensor sparse coding together. Finally, alternating direction method of multipliers is used to solve the optimization problem. Experimental results on three data sets and one real data set demonstrate that the proposed method substantially outperforms the existing state-ofthe-art HSI super-resolution methods.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2019.2893530