  Hyperspectral images (HSIs) with narrow spectral bands can capture rich
spectral information, making them suitable for many computer vision tasks. One
of the fundamental limitations of HSI is its low spatial resolution, and
several recent works on super-resolution(SR) have been proposed to tackle this
challenge. However, due to HSI cameras' diversity, different cameras capture
images with different spectral response functions and the number of total
channels. The existing HSI datasets are usually small and consequently
insufficient for modeling. We propose a Meta-Learning-Based
Super-Resolution(MLSR) model, which can take in HSI images at an arbitrary
number of input bands' peak wavelengths and generate super-resolved HSIs with
an arbitrary number of output bands' peak wavelengths. We artificially create
sub-datasets by sampling the bands from NTIRE2020 and ICVL datasets to simulate
the cross-dataset settings and perform HSI SR with spectral interpolation and
extrapolation on them. We train a single MLSR model for all sub-datasets and
train dedicated baseline models for each sub-dataset. The results show the
proposed model has the same level or better performance compared to
the-state-of-the-art HSI SR methods.
