  We describe a novel method for blind, single-image spectral super-resolution.
While conventional super-resolution aims to increase the spatial resolution of
an input image, our goal is to spectrally enhance the input, i.e., generate an
image with the same spatial resolution, but a greatly increased number of
narrow (hyper-spectral) wave-length bands. Just like the spatial statistics of
natural images has rich structure, which one can exploit as prior to predict
high-frequency content from a low resolution image, the same is also true in
the spectral domain: the materials and lighting conditions of the observed
world induce structure in the spectrum of wavelengths observed at a given
pixel. Surprisingly, very little work exists that attempts to use this
diagnosis and achieve blind spectral super-resolution from single images. We
start from the conjecture that, just like in the spatial domain, we can learn
the statistics of natural image spectra, and with its help generate finely
resolved hyper-spectral images from RGB input. Technically, we follow the
current best practice and implement a convolutional neural network (CNN), which
is trained to carry out the end-to-end mapping from an entire RGB image to the
corresponding hyperspectral image of equal size. We demonstrate spectral
super-resolution both for conventional RGB images and for multi-spectral
satellite data, outperforming the state-of-the-art.
