  Hyperspectral imaging can help better understand the characteristics of
different materials, compared with traditional image systems. However, only
high-resolution multispectral (HrMS) and low-resolution hyperspectral (LrHS)
images can generally be captured at video rate in practice. In this paper, we
propose a model-based deep learning approach for merging an HrMS and LrHS
images to generate a high-resolution hyperspectral (HrHS) image. In specific,
we construct a novel MS/HS fusion model which takes the observation models of
low-resolution images and the low-rankness knowledge along the spectral mode of
HrHS image into consideration. Then we design an iterative algorithm to solve
the model by exploiting the proximal gradient method. And then, by unfolding
the designed algorithm, we construct a deep network, called MS/HS Fusion Net,
with learning the proximal operators and model parameters by convolutional
neural networks. Experimental results on simulated and real data substantiate
the superiority of our method both visually and quantitatively as compared with
state-of-the-art methods along this line of research.
