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Evolutionary programming in image restoration via reduced order model Kalman filtering

The image restoration via reduced order model Kalman filter (ROMKF) is accomplished in conjunction with a maximum likelihood technique for image/blur parameter estimation purposes. Traditionally, one uses initial condition sensitive optimization algorithms at the estimation stage. This work concerns the use of evolutionary programming (EP) in the parameter estimation phase of the ROMKF space-adaptive image restoration. Experimental comparisons between both of the mentioned optimization strategies are presented. Simulation results suggest that more reliable ROMKF restorations are obtained when less initial condition sensitive algorithms are adopted.