  In recent times, CNNs have made significant contributions to applications in
image generation, super-resolution and style transfer. In this paper, we build
upon the work of Howard and Gugger, He et al. and Misra, D. and propose a CNN
architecture that accurately reconstructs hyperspectral images from their RGB
counterparts. We also propose a much shallower version of our best model with a
10% relative memory footprint and 3x faster inference, thus enabling real-time
video applications while still experiencing only about a 0.5% decrease in
performance.
