Novel Optimization Framework to Recover True Image Data

This paper focuses on the restoration of spatial degradations that appear in images due to invariant or variant blurs and additive noise. It is one of the basic problems of visual information processing systems. The problem possesses issues of complexity, huge volume of data, uncertainty and a real-time response in critical applications. In this paper, a new optimization framework for restoration is proposed to solve the problem effectively. The proposed solution is modeled as constrained optimization of huge vectors, each representing a grayscale image in spatial domain. In the proposed framework, particle swarm optimization-based evolution is adopted to minimize the modified error estimate (MEE) for better restoration. The framework added hyperheuristic layer to combine local and global search properties. Therefore, randomness in the evolution, augmented with apriori knowledge from the problem domain, assisted in achieving the objective. In addition, an adaptive weighted regularization scheme is proposed in MEE to cater with the uncertainty due to ill-posed nature of the inverse problem. The visual and quantitative results are provided to endorse the effectiveness of the proposed framework in maximizing signal-to-noise ratio and minimizing well-known error measures in contrast to existing restoration methods.