Geodesic-based and projection-based neural blind deconvolution algorithms

The present contribution studies a geodesic-based and a projection-based learning algorithm over a curved parameter space for blind deconvolution (BD) application. The chosen deconvolving structure appears as a single neuron model whose learning rules naturally arise from criterion-function minimization over a smooth manifold. We consider the BD performances of the two classes of algorithms as well as their computational burden. Also, numerical comparisons with seven BD algorithms known from the scientific literature are illustrated and discussed.