A Novel Approach for Regularized Signal Deconvolution Based on Hybrid Swarm Intelligence: Application to Neutron Radiography

In this work, we introduce a new approach for the signal deconvolution problem, which is useful for the enhancement of neutron radiography projections. We attempt to restore original signals and get rid of noise present during acquisition or processing, due to gamma radiations or randomly distributed neutron flux. Signal deconvolution is an ill-posed inverse problem, so regularization techniques are used to smooth solutions by imposing constraints in the objective function. Various popular algorithms have been developed to solve such problem. This paper proposes a new approach to the nonlinear degraded signals restoration which is useful in many signal enhancement applications, based on a synergy of two swarm intelligence algorithms: particle swarm optimization (PSO) and bacterial foraging optimization (BFO) applied for total variation (TV) minimization, instead of the standard Tikhonov regularization method. We attempt to reconstruct or recover signals using some a priori knowledge of the degradation phenomenon. The truncated singular value decomposition and the wavelet filtering methods are also considered in this paper. A comparison between several powerful techniques is conducted.