High-resolution spectral image reconstruction based on compressed data fusion

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.