Stochastic Deep Image Prior for Multishot Compressive Spectral Image Fusion

The Deep Image Prior (DIP) technique has been successfully employed in Compressive Spectral Imaging (CSI) as a non-data-driven deep model approach. DIP methodology updates the deep network’s weights by minimizing a loss function that considers the difference between the measurements and the forward operator of the network’s output. However, this method often yields local minima as all the measurements are evaluated at each iteration. This paper proposes a stochastic deep image prior (SDIP) approach, which stochastically trains DIP networks using random subsets of measurements from different CSI sensors in a CSI fusion (CSIF) setting, resulting in the improvement of the convergence through stochastic gradient descent optimization. The proposed SDIP method improves upon the deterministic DIP and requires less computational time since fewer forward operators are required per iteration. The SPID method provides comparable performance against the state-of-the-art CSF techniques based on supervised data-driven and unsupervised methods, achieving up to 5 dB in the reconstruction.