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Novel Optimization Framework to Recover True Image Data

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Abstract

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.

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Acknowledgments

Authors would acknowledge Higher Education Commission (HEC) of Pakistan, for its continuous financial support in the meritorious role of scholarship for higher education and the anonymous reviewers for their many valuable comments and suggestions that helped to improve this paper. ‘Some/all of the data presented in this paper were obtained from the Mikulski Archive for Space Telescopes (MAST). STScI is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS5-26555. Support for MAST for non-HST data is provided by the NASA Office of Space Science via grant NNX13AC07G and by other grants and contracts.’

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Authors and Affiliations

  1. Computer Science Department, National University of Computer and Emerging Sciences, Islamabad, Pakistan

    Mohsin Bilal & Hasan Mujtaba

  2. College of Computer and Information Systems, Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia

    Muhammad Arfan Jaffar

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  1. Mohsin Bilal
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  2. Hasan Mujtaba
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  3. Muhammad Arfan Jaffar
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Correspondence to Mohsin Bilal.

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Bilal, M., Mujtaba, H. & Jaffar, M.A. Novel Optimization Framework to Recover True Image Data. Cogn Comput 7, 680–692 (2015). https://doi.org/10.1007/s12559-015-9339-7

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