An Iterative Regularization Method based on Tensor Subspace Representation for Hyperspectral Image Super-Resolution

Homepage: https://liangjiandeng.github.io/ and https://tingxu113.github.io/

How to use?

• Directly run: Demo.m

Citation

@ARTICLE{xu2022tgrs,
author={T. Xu, T.-Z. Huang, L.-J. Deng, and N. Yokoya},
booktitle={IEEE Trans. Geosci. Remote Sens.},
title={An Iterative Regularization Method based on Tensor Subspace Representation for Hyperspectral Image Super-Resolution},
year={2022},
pages={},
}

Method

Motivation: Statistical analysis of low tensor tubal rank on seven HR-HSIs (the same color represents the same dataset). (a)–(f) Normalized tensor singular value curve of original HR-HSI and five permuted HR-HSIs, respectively.

(a) Diagram of TenSR and MatSR. (b) HSI-SR results by using TenSR and MatSR with different subspace dimensions.

The spatial–spectral nonlocal self-similarity property of the tensor coefficient.

Overall Framework: The framework of our model. The details of our framework can be found in Sect. III.

Final Model:

Iterative Regularization Algorithm:

Visual Results: HSI-SR results of Indian Pines. The first and second rows present the results consisting of 11th, 48th, and 128th band of the fused images under SNR = 15 dB and the corresponding error maps, respectively. The blue block shows the representation region.

Quantitative Results: Quantitative evaluation of different methods on Indian Pines dataset with four noise cases