  Hyperspectral imaging is one of the most promising techniques for
intraoperative tissue characterisation. Snapshot mosaic cameras, which can
capture hyperspectral data in a single exposure, have the potential to make a
real-time hyperspectral imaging system for surgical decision-making possible.
However, optimal exploitation of the captured data requires solving an
ill-posed demosaicking problem and applying additional spectral corrections to
recover spatial and spectral information of the image. In this work, we propose
a deep learning-based image demosaicking algorithm for snapshot hyperspectral
images using supervised learning methods. Due to the lack of publicly available
medical images acquired with snapshot mosaic cameras, a synthetic image
generation approach is proposed to simulate snapshot images from existing
medical image datasets captured by high-resolution, but slow, hyperspectral
imaging devices. Image reconstruction is achieved using convolutional neural
networks for hyperspectral image super-resolution, followed by cross-talk and
leakage correction using a sensor-specific calibration matrix. The resulting
demosaicked images are evaluated both quantitatively and qualitatively, showing
clear improvements in image quality compared to a baseline demosaicking method
using linear interpolation. Moreover, the fast processing time of~45\,ms of our
algorithm to obtain super-resolved RGB or oxygenation saturation maps per image
frame for a state-of-the-art snapshot mosaic camera demonstrates the potential
for its seamless integration into real-time surgical hyperspectral imaging
applications.
