Hyperspectral imaging (HSI) has become an invaluable imaging tool for many applications in astrophysics or Earth observation. Unfortunately, direct observation of hyperspectral images is impossible since the actual measurements are 2-D and suffer from strong spatial and spectral degradations, especially in the infrared. We present in this work an original method for high-resolution hyperspectral image reconstruction from heterogeneous 2-D measurements degraded by integral field spectroscopy (IFS) instrument. A fundamental part of this work is developing a forward model that accounts for the limitations of the IFS instrument, such as wavelength-dependent spatial and spectral blur, subsampling, and inhomogeneous sampling steps. The reconstruction method inverts the forward model using a deterministic regularization framework for edge-preserving. It fuses information from different observations and spectral bands for resolution enhancements. We rely on the Majorize-Minimize memory gradient (3MG) optimization algorithm to solve the inverse problem while considering a low-rank approximation for the unknown to handle the high-dimensionality of the problem.
