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. Traditional blind image algorithms impose various constraints and preconditions on the image and blur domains, resulting in highly inflexible schemes. They often experience interdomain dependency and tend to be trapped at poor local minima, leading to mediocre image restorations and blur identifications. The new technique addresses these issues by developing 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 identifications. 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.
computational reinforced learning blind image deconvolution evolutionary optimization