  Despite the great success of deep model on Hyperspectral imagery (HSI)
super-resolution(SR) for simulated data, most of them function unsatisfactory
when applied to the real data, especially for unsupervised HSI SR methods. One
of the main reason comes from the fact that the predefined degeneration models
(e.g. blur in spatial domain) utilized by most HSI SR methods often exist great
discrepancy with the real one, which results in these deep models overfit and
ultimately degrade their performance on real data. To well mitigate such a
problem, we explore the unsupervised blind HSI SR method. Specifically, we
investigate how to effectively obtain the degeneration models in spatial and
spectral domain, respectively, and makes them can well compatible with the
fusion based SR reconstruction model. To this end, we first propose an
alternating optimization based deep framework to estimate the degeneration
models and reconstruct the latent image, with which the degeneration models
estimation and HSI reconstruction can mutually promotes each other. Then, a
meta-learning based mechanism is further proposed to pre-train the network,
which can effectively improve the speed and generalization ability adapting to
different complex degeneration. Experiments on three benchmark HSI SR datasets
report an excellent superiority of the proposed method on handling blind HSI
fusion problem over other competing methods.
