Efficient and Accurate Hyperspectral Image Demosaicing with Neural
  Network Architectures
Eric L. Wisotzky Lara Wallburg Anna Hilsmann Peter Eisert Thomas Wittenberg Stephan Göb
Neural network architectures for image demosaicing have been become more and
more complex. This results in long training periods of such deep networks and
the size of the networks is huge. These two factors prevent practical
implementation and usage of the networks in real-time platforms, which
generally only have limited resources. This study investigates the
effectiveness of neural network architectures in hyperspectral image
demosaicing. We introduce a range of network models and modifications, and
compare them with classical interpolation methods and existing reference
network approaches. The aim is to identify robust and efficient performing
network architectures. Our evaluation is conducted on two datasets,
"SimpleData" and "SimRealData," representing different degrees of realism in
multispectral filter array (MSFA) data. The results indicate that our networks
outperform or match reference models in both datasets demonstrating exceptional
performance. Notably, our approach focuses on achieving correct spectral
reconstruction rather than just visual appeal, and this emphasis is supported
by quantitative and qualitative assessments. Furthermore, our findings suggest
that efficient demosaicing solutions, which require fewer parameters, are
essential for practical applications. This research contributes valuable
insights into hyperspectral imaging and its potential applications in various
fields, including medical imaging.