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An Iterative Regularization Method Based on Tensor Subspace Representation for Hyperspectral Image Super-Resolution
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DOI：10.1109/TGRS.2022.3176266.png)
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Abstract
En 中文
Hyperspectral image super-resolution (HSI-SR) can be achieved by fusing a paired multispectral image (MSI) and hyperspectral image (HSI), which is a prevalent strategy. But, how to precisely reconstruct the high spatial resolution hyperspectral image (HR-HSI) by fusion technology is a challenging issue. In this article, we propose an iterative regularization method based on tensor subspace representation (IR-TenSR) for MSI-HSI fusion, thus HSI-SR. First, we propose a tensor subspace representation (TenSR)-based regularization model that integrates the global spectral-spatial low-rank and the nonlocal self-similarity priors of HR-HSI. These two priors have been proven effective, but previous HSI-SR works cannot simultaneously exploit them. Subsequently, we design an iterative regularization procedure to utilize the residual information of acquired low-resolution images, which are ignored in other works that produce suboptimal results. Finally, we develop an effective algorithm based on the proximal alternating minimization method to solve the TenSR-regularization model. With that, we obtain the iterative regularization algorithm. Experiments implemented on the simulated and real datasets illustrate the advantages of the proposed IR-TenSR compared with the state-of-the-art fusion approaches. The code is available at https://github.com/liangjiandeng/IR_TenSR.
Keywords:
Tensors
Correlation
Spatial resolution
Hyperspectral imaging
Task analysis
Superresolution
Iterative algorithms
Global spatial-spectral low-rank prior
hyperspectral image super-resolution
iterative regularization
nonlocal self-similarity
proximal alternating minimization
tensor subspace
Journal
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8.6
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2.1W
Citations:
10.7W