Hyperspectral Image Super-resolution with Deep Priors and Degradation Model Inversion
This work addresses the problem of enhancing spatial resolution in hyperspectral imaging for applications like remote sensing, but it is incremental as it builds on existing deep learning approaches by adding model-based elements.
The paper tackles hyperspectral image super-resolution by fusing low-resolution hyperspectral and high-resolution RGB images, proposing a method that incorporates a linear degradation model and deep priors, achieving performance improvements as shown in experiments.
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.