Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Novel Optimization Framework to Recover True Image Data

  • Published:
Cognitive Computation Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Hansan PC, Nagy JG, O’leary DP. Deblurring images, matrices, spectra and filtering. Philadelphia: SIAM; 2006.

    Book  Google Scholar 

  2. Annadurai S, Shanmugalakshmi R. Fundamental of digital image processing. Delhi: Pearson; 2006.

    Google Scholar 

  3. Gonzalez RC, Woods R. Digital image processing. 3rd ed. USA: Prentice Hall; 2008.

    Google Scholar 

  4. Perry SW, Guan L. Weight assignment for adaptive image restoration by neural networks. IEEE Trans Neural Netw. 2000;11(1):156–70.

    Article  CAS  PubMed  Google Scholar 

  5. Bar L, Sochen N, Kiryati N. Restoration of images with piecewise space-variant blur. In: SSVM’07 Proceedings of 1st international conference scale space methods and variational methods in computer vision. 2000. pp. 533–544.

  6. Bardsley J, Jefferies S, Nagy J, Plemmons R. A computational method for the restoration of images with an unknown, spatially-varying blur. Opt Express. 2006;14(5):1776–82.

    Article  Google Scholar 

  7. Belokogn I, Carbillet M, Chesneau O. How to push the limits of evolved stars observations with SPHERE/VLT, 2011. http://gaia.oca.eu/IMG/pdf/belokogne-m2-carbillet-fizeau-valrose.pdf. Accessed 1 Jan 2014.

  8. Mignotte M. A segmentation-based regularization term for image deconvolution. IEEE Trans Image Process. 2006;15(7):1973–84.

    Article  PubMed  Google Scholar 

  9. Bilal M, Rehman MS, Jaffar MA. Evolutionary reconstruction: image restoration for space variant degradation. Smart Comput Rev. 2013;3(4):220–32.

    Article  Google Scholar 

  10. Bilal M, Jaffar MA, Hussain A, Shim SO. Optimal edge preserving restoration with efficient regularisation. Imaging Sci J. 2015;63(2):68–75.

    Article  Google Scholar 

  11. Bilal M, Sharif M, Jaffar MA, Hussain A, Mirza AM. Image restoration using modified hopfield fuzzy regularization method. In: 5th International conference on future information technology (FutureTech) 2010. pp. 1–6.

  12. Zhao B, Zhang W, Ding H, Wang H. Non-blind image deblurring from a single image. Cognit Comput. 2013;5(1):3–12.

    Article  Google Scholar 

  13. Bilal M, Hussain A, Jaffar MA, Choi T, Mirza AM. Estimation and optimization based ill-posed inverse restoration using fuzzy logic. Multimed Tools Appl. 2014;69(3):1067–87.

    Article  Google Scholar 

  14. Al-Rifaie MM, Bishop JM, Caines S. Creativity and autonomy in swarm intelligence systems. Cognit Comput. 2012;4(3):320–31.

    Article  Google Scholar 

  15. Swan J, Woodward J, Özcan E, Kendall G, Burke E. Searching the hyper-heuristic design space. Cognit Comput. 2014;6(1):66–73.

    Article  Google Scholar 

  16. Welk M, Theis D, Weickert J. Variational deblurring of images with uncertain and spatially variant blurs. In: Kropatsch W, Sablatnig R, Hanbury A, editors. DAGM 2005, LNCS 3663. pp. 485–492.

  17. Klapp I, Sochen N, Mendlovic D. Deblurring space-variant blur by adding noisy image. In: Proceedings of SSVM 2012, LNCS 6667. pp. 157–168.

  18. Kober V, Agis JG. Space-variant restoration with sliding discrete cosine transform. In: Kropatsch WG, Kampel M, Hanbury A, editors. CAIP 2007, LNCS 4673. pp. 903–911.

  19. Portilla J, Simoncelli E. Image restoration using gaussian scale mixtures in the wavelet domain. In: Proceedings of 10th international conference image process. Barcelona, Spain; 2003. pp. 14–17.

  20. Portilla J, Strela V. Image denoising using gaussian scale mixtures in the wavelet domain. Computer science technical report # TR2002-831. New York: Courant Institute of Mathematical Sciences, New York University; 2002.

    Google Scholar 

  21. Guerrero JA, Manceraa L, Portilla J. Image restoration using space-variant gaussian scale mixtures in overcomplete pyramids. IEEE Trans Image Process. 2008;17(1):27–41.

    Article  Google Scholar 

  22. Nagy JG, O’Leary DP. Restoring images degraded by spatially-variant blur. SIAM J Sci Comput. 1998;19:1063–82.

    Article  Google Scholar 

  23. Gonzalez RC, Woods RE, Eddins SL. Digital image processing using MATLAB. 2nd ed. Knoxville, TN: Gatesmark Publishing; 2009.

    Google Scholar 

  24. Faisal M, Lanterman AD, Snyder DL, White RL. Implementation of a modified Richardson-Lucy method for image restoration on a massively parallel computer to compensate for space-variant point spread of a charge-coupled-device camera. J Opt Soc Am A 1995;12(12):2593–603.

    Article  Google Scholar 

  25. Boden AF, Redding DC, Hanisch RJ, Mo J. Massively parallel spatially-variant maximum likelihood image restoration. In: Jacoby GH, Barnes J, editors. Astronomical data analysis software and systems V 1996, (101) of astronomical society of the pacific conference series, pp. 131.

  26. Dell’Acqua P, Serra-Capizzano S, Tablino Possio C. Optimal preconditioning for image deblurring with anti-reflective boundary conditions, ArXiv e-prints. 2012; arXiv 1211.0393.

  27. Le J, Jin H, Xiaoguang LV, Liu J. A new efficient alternating method for image restoration and texture extraction. J Comput Inf Syst. 2013;9(7):2595–602.

    Google Scholar 

  28. Liu J, Huang TZ, Lv XG, Wang S. An efficient variational method for image restoration. In: Hindawi Publishing Corporation, Abstract and applied analysis 2013(213536). pp. 1–11.

  29. Huang C, Ding X, Fang C, Wen D. Robust image restoration via adaptive low-rank approximation and joint kernel regression. IEEE Trans Image Process. 2014;23(12):5284–97.

    Article  PubMed  Google Scholar 

  30. Zhang X, Sun F, Liu G, Ma Y. Non-blind deblurring of structured images with geometric deformation. Vis Comput. 2015;31:131–40.

    Article  Google Scholar 

  31. Clerc M, Kennedy J. The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evolut Comput. 2002;6(1):58–73.

    Article  Google Scholar 

  32. Zhou W, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process. 2004;13(4):600–12.

    Article  Google Scholar 

Download references

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.’

Author information

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

Authors
  1. Mohsin Bilal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hasan Mujtaba
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Muhammad Arfan Jaffar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohsin Bilal.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-015-9339-7

Keywords

Search

Navigation