MATLAB PROJECT
Nonlocal Patch Tensor
Sparse Representation for Hyperspectral Image Super-Resolution
Abstract:
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.
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