Spectral filters design for a better hyperspectral reconstruction

Spectral reconstruction (recovering spectra from RGB measurements) is a vital problem of computational photography. As a matter of curiosity, modern mobile devices open a new opportunity to improve the quality of spectral reconstruction by utilizing images from several cameras at once. This leads to the idea of creating a mobile hyperspectral camera for the general public. In this paper we investigate the achievable accuracy when using several identical cameras simultaneously in combination with different spectral filters. To find optimal filters, two algorithms are proposed: one learns spectral transmittance functions simultaneously with spectral reconstruction, the other learns only spectral transmittances by information loss minimization. As a result of numerical experiments, 4 cameras and 4 filters allow us to perform spectral reconstruction two times accurately than from a single RGB image.