  Hyperspectral image (HSI) super-resolution is commonly used to overcome the
hardware limitations of existing hyperspectral imaging systems on spatial
resolution. It fuses a low-resolution (LR) HSI and a high-resolution (HR)
conventional image of the same scene to obtain an HR HSI. In this work, we
propose a method that integrates a physical model and deep prior information.
Specifically, a novel, yet effective two-stream fusion network is designed to
serve as a {regularizer} for the fusion problem. This fusion problem is
formulated as an optimization problem whose solution can be obtained by solving
a Sylvester equation. Furthermore, the regularization parameter is
simultaneously estimated to automatically adjust contribution of the physical
model and {the} learned prior to reconstruct the final HR HSI. Experimental
results on {both simulated and real data} demonstrate the superiority of the
proposed method over other state-of-the-art methods on both quantitative and
qualitative comparisons.
