Abstract: A method of reconstructing a hyperspectral data cube from an encoded and/or spectrally sheared image and an unencoded and spectrally unsheared image of a scene comprises providing the encoded and/or spectrally sheared image and an unencoded and spectrally unsheared image to a neural network comprising an encoder-decoder structure having an encoder portion and a decoder portion; performing one or more primary convolution operations using the encoder portion; and performing one or more transpose convolution operations using the decoder portion; performing spatially adaptive normalization and/or inverted attention normalization on an output of primary convolution operations and/or transpose convolution operations wherein the spatially adaptive normalization and/or inverted attention normalization is performed using a representation of an encoding scheme used to encode the at least one encoded and/or spectrally sheared image and an unencoded and spectrally unsheared image of the scene.

Abstract: Computer implemented methods (600) based on artificial neural networks for determining the datacube are also disclosed.