Semi-supervised deep learning techniques for spectrum reconstruction from RGB images

In this thesis we introduce some techniques reaching close to state of the art performance in the topic of spectrum reconstruction while requiring in input only a few HS images or even pixels. We also show the importance of exploiting the physical model in the training pipeline to constraint the output and ease out the whole process.