  Hyperspectral imaging holds promises in surgical imaging by offering
biological tissue differentiation capabilities with detailed information that
is invisible to the naked eye. For intra-operative guidance, real-time spectral
data capture and display is mandated. Snapshot mosaic hyperspectral cameras are
currently seen as the most suitable technology given this requirement. However,
snapshot mosaic imaging requires a demosaicking algorithm to fully restore the
spatial and spectral details in the images. Modern demosaicking approaches
typically rely on synthetic datasets to develop supervised learning methods, as
it is practically impossible to simultaneously capture both snapshot and
high-resolution spectral images of the exact same surgical scene. In this work,
we present a self-supervised demosaicking and RGB reconstruction method that
does not depend on paired high-resolution data as ground truth. We leverage
unpaired standard high-resolution surgical microscopy images, which only
provide RGB data but can be collected during routine surgeries. Adversarial
learning complemented by self-supervised approaches are used to drive our
hyperspectral-based RGB reconstruction into resembling surgical microscopy
images and increasing the spatial resolution of our demosaicking. The spatial
and spectral fidelity of the reconstructed hyperspectral images have been
evaluated quantitatively. Moreover, a user study was conducted to evaluate the
RGB visualisation generated from these spectral images. Both spatial detail and
colour accuracy were assessed by neurosurgical experts. Our proposed
self-supervised demosaicking method demonstrates improved results compared to
existing methods, demonstrating its potential for seamless integration into
intra-operative workflows.
