A Neurodynamic Optimization Approach for L1 Minimization with Application to Compressed Image Reconstruction

This paper presents a neurodynamic optimization approach for l 1 minimization based on an augmented Lagrangian function. By using the threshold function in locally competitive algorithm (LCA), subgradient at a nondifferential point is equivalently replaced with the difference of the neuronal state and its mapping. The efficacy of the proposed approach is substantiated by reconstructing three compressed images.