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Image Super Resolution Reconstruction Using Iterative Adaptive Regularization Method and Genetic Algorithm

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Computational Intelligence in Data Mining - Volume 2

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 32))

Abstract

Super resolution is a technique to obtain high resolution images from several degraded low-resolution images. This has got attention in the research society because of its wide use in many fields of science and technology. Even though many methods exist for super resolution, adaptive regularization method is preferred because of its simplicity and the constraints used to get better image restoration result. In this paper first adaptive algorithm is considered to restore better edge and texture of image. Further Genetic algorithm is used to smooth the noise and better frequency addition into the image to get an optimum super resolution image.

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Author information

Authors and Affiliations

  1. AMET University, Chennai, Tamil Nadu, India

    S. S. Panda

  2. Roland Institute of Technology, Berhampur, Odisha, India

    G. Jena & S. K. Sahu

Authors
  1. S. S. Panda
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  2. G. Jena
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  3. S. K. Sahu
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Corresponding author

Correspondence to S. S. Panda .

Editor information

Editors and Affiliations

  1. University of Canberra, Canberra, Australia and University of South Australia, Adelaide, South Australia, Australia

    Lakhmi C. Jain

  2. Department of Computer Science and Engineering, Veer Surendra Sai University of Technology, Sambalpur, Odisha, India

    Himansu Sekhar Behera

  3. Computer Science & Engineering, Kalyani University, Nadia, West Bengal, India

    Jyotsna Kumar Mandal

  4. Dept. of Computer Science and Engineering, National Institute of Technology Rourkela, Rourkela, India

    Durga Prasad Mohapatra

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Panda, S.S., Jena, G., Sahu, S.K. (2015). Image Super Resolution Reconstruction Using Iterative Adaptive Regularization Method and Genetic Algorithm. In: Jain, L., Behera, H., Mandal, J., Mohapatra, D. (eds) Computational Intelligence in Data Mining - Volume 2. Smart Innovation, Systems and Technologies, vol 32. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2208-8_62

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  • DOI: https://doi.org/10.1007/978-81-322-2208-8_62

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2207-1

  • Online ISBN: 978-81-322-2208-8

  • eBook Packages: EngineeringEngineering (R0)

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