  Hyperspectral (HS) images contain detailed spectral information that has
proven crucial in applications like remote sensing, surveillance, and
astronomy. However, because of hardware limitations of HS cameras, the captured
images have low spatial resolution. To improve them, the low-resolution
hyperspectral images are fused with conventional high-resolution RGB images via
a technique known as fusion based HS image super-resolution. Currently, the
best performance in this task is achieved by deep learning (DL) methods. Such
methods, however, cannot guarantee that the input measurements are satisfied in
the recovered image, since the learned parameters by the network are applied to
every test image. Conversely, model-based algorithms can typically guarantee
such measurement consistency. Inspired by these observations, we propose a
framework that integrates learning and model based methods. Experimental
results show that our method produces images of superior spatial and spectral
resolution compared to the current leading methods, whether model- or DL-based.
