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Image Super Resolution Using Wavelet Transformation Based Genetic Algorithm

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Computational Intelligence in Data Mining—Volume 2

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 411))

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Abstract

Super resolution became one of the best techniques to obtain high resolution images as of a number of low-resolution images because of its simplicity and wide range of application in many fields of science and technology. There are several methods exist for super resolution but, wavelet transformation is chosen because of its minimalism and the constraints used to get better image restoration result. In this paper first Wavelet Transformation is considered to restore better 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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References

  1. Bing, T., Qing, X., Xun, G., Shuai, S.: Super-resolution image reconstruction technology development status of the Information Engineering University. 12(4), 4 (2003)

    Google Scholar 

  2. Huang, L.-L., Xiao, L., Wei, Z.-H.: Efficient and effective total variation image super-resolution: a preconditioned operator splitting approach. Math. Prob. Eng. (2011)

    Google Scholar 

  3. Borman, S., Stevenson, R.: Spatial resolution enhance-ment of low-resolution image sequences a comprehensive reviewwith directions for future research [J/OL]. http://citeseer.nj.nec.com

  4. Hansen, P.C, O’Leary, D.P.: The use of the L-curve in the regularization of discrete ill-posed problems. SIAM J. Sci. Comput. 14(6), 1487–1503 (1993)

    Google Scholar 

  5. Wu gold.: Adaptive regularized image restoration (Ph.D. Thesis). National Defense University, (2006)

    Google Scholar 

  6. Geman, D., Yang, C.: Nonlinear image recovery with half-quadratic regularization [J]. IEEE Trans. Image Process. 4(7), 932–946 (1995)

    Article  Google Scholar 

  7. Lagendijk, Reginald L., Biemond, Jan, Boekee, Dick E.: Regularized iterative image restoration with ringing reduction. IEEE Trans. Acoust. Speech Signal Process. 36(12), 1874–1888 (1988)

    Article  MATH  Google Scholar 

  8. Kang, M.G., Katsaggelos, A.K., Schafer, R.W.: A regularized iterative image restoration algorithm. IEEE Trans. Signal Process. 39(4), 4 (1991)

    Google Scholar 

  9. Xi, Y., Chai, T., Yun, W.: Summarization of genetic algorithm. Control Theory Appl. (6), 697–708 (1996)

    Google Scholar 

  10. Efrat, N., et al. Accurate blur models vs. image priors in single image super-resolution. 2013 IEEE International Conference on Computer Vision (ICCV) IEEE, (2013)

    Google Scholar 

  11. Panda, S.S., Jena, G., Sahu, S.K.: Image super resolution reconstruction using iterative adaptive regularization method and genetic algorithm. In: Computational Intelligence in Data Mining—Volume 2, pp. 675–681. Springer India (2015)

    Google Scholar 

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Author information

Authors and Affiliations

  1. AMET University, Chennai, India

    Sudam Sekhar Panda

  2. Sir CR Reddy College of Engineering, Eluru, India

    Sudam Sekhar Panda

  3. Roland Institute of Technology, Berhampur, India

    Gunamani Jena

Authors
  1. Sudam Sekhar Panda
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  2. Gunamani Jena
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Corresponding author

Correspondence to Sudam Sekhar Panda .

Editor information

Editors and Affiliations

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

    Himansu Sekhar Behera

  2. Department of Computer Science and Engineering, National Institute of Technology, Rourkela, India

    Durga Prasad Mohapatra

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© 2016 Springer India

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Panda, S.S., Jena, G. (2016). Image Super Resolution Using Wavelet Transformation Based Genetic Algorithm. In: Behera, H., Mohapatra, D. (eds) Computational Intelligence in Data Mining—Volume 2. Advances in Intelligent Systems and Computing, vol 411. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2731-1_33

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  • DOI: https://doi.org/10.1007/978-81-322-2731-1_33

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2729-8

  • Online ISBN: 978-81-322-2731-1

  • eBook Packages: EngineeringEngineering (R0)

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