A computational reinforced learning scheme to blind image deconvolution

This paper presents a new approach to adaptive blind image deconvolution based on computational reinforced learning in an attractor-embedded solution space. The new technique develops an evolutionary strategy that generates the improved blur and image populations progressively. A dynamic attractor space is constructed by integrating the knowledge domain of the blur structures into the algorithm. The attractors are predicted using a maximum a posteriori estimator and their relevance is evaluated with respect to the computed blurs. We develop a novel reinforced mutation scheme that combines stochastic search and pattern acquisition throughout the blur identification. It enhances the algorithmic convergence and reduces the computational cost significantly. The new technique is robust in alleviating the constraints and difficulties encountered by most conventional methods. Experimental results show that the new algorithm is effective in restoring the degraded images and identifying the blurs.