Hyperspectral Image Super-resolution with Deep Priors and Degradation Model Inversion

• URL: http://arxiv.org/abs/2201.09851v1
• Date: Mon, 24 Jan 2022 18:17:40 GMT
• Title: Hyperspectral Image Super-resolution with Deep Priors and Degradation Model Inversion
• Authors: Xiuheng Wang, Jie Chen, C\'edric Richard
• Abstract summary: Fusion-based hyperspectral image (HSI) super-resolution is attracting increasing attention. Deep learning architectures have been used to address the HSI super-resolution problem. We propose a method that makes use of the linear degradation model in the data-fidelity term of the objective function.
• Score: 6.0622962428871885
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To overcome inherent hardware limitations of hyperspectral imaging systems with respect to their spatial resolution, fusion-based hyperspectral image (HSI) super-resolution is attracting increasing attention. This technique aims to fuse a low-resolution (LR) HSI and a conventional high-resolution (HR) RGB image in order to obtain an HR HSI. Recently, deep learning architectures have been used to address the HSI super-resolution problem and have achieved remarkable performance. However, they ignore the degradation model even though this model has a clear physical interpretation and may contribute to improve the performance. We address this problem by proposing a method that, on the one hand, makes use of the linear degradation model in the data-fidelity term of the objective function and, on the other hand, utilizes the output of a convolutional neural network for designing a deep prior regularizer in spectral and spatial gradient domains. Experiments show the performance improvement achieved with this strategy.

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