Compressive spectral imagers reduce the number of sampled pixels by coding and combining the spectral information. However, sampling compressed information with simultaneous high spatial and high spectral resolution demands expensive high-resolution sensors. This work introduces a model allowing data from high spatial/low spectral and low spatial/high spectral resolution compressive sensors to be fused. Based on this model, the compressive fusion process is formulated as an inverse problem that minimizes an objective function defined as the sum of a quadratic data fidelity term and smoothness and sparsity regularization penalties. The parameters of the different sensors are optimized and the choice of an appropriate regularization is studied in order to improve the quality of the high resolution reconstructed images. Simulation results conducted on synthetic and real data, with different CS imagers, allow the quality of the proposed fusion method to be appreciated.

Compressive spectral imagers drastically reduce the number of sampled pixels by performing linear combinations of coded spectral information. However, compressing information with simultaneously high spatial and high spectral resolutions demands expensive high-resolution sensors. This work introduces a model allowing compressive data from high spatial/low spectral and low spatial/high spectral resolution sensors to be fused. The sensing matrix of this model is designed carefully to be incoherent with the dictionary associated with the unknown image. Based on this model, the compressive fusion process is formulated as an inverse problem that minimizes an objective function defined as the sum of a quadratic data fidelity term and smoothness and sparsity regularization penalties.