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Restoration of Blurred-Noisy Images Through the Concept of Bilevel Programming

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Advances in Visual Computing (ISVC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9475))

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Abstract

Finding a compromise between regularity to remove noise and preserving image fidelity for natural images is unarguably a non-trivial problem. This paper proposes a new image restoration algorithm that executes an optimal tradeoff between sharpness and noise to warrant an acceptable result of image restoration based on bilevel programming. The algorithm demands an objective functions to perform denoising on the degraded image for the lower-level problem using the curvelet-based denoising method, while the upper-level problem with ultimate objective function that is to obtain restored image by performing deblurring to the denoised image using an improved Wiener filter. Experiments were conducted for synthetically blurred and noisy images. The experimental result shows that the algorithm successfully restores image detail. Numerical measurements of the image quality reveal that the algorithm is comparable with other state-of-the-art methods and has the advantage for image contrast and preserving edge details.

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Author information

Authors and Affiliations

  1. Center of Image and Signal Processing, Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Jessica Soo Mee Wong & Chee Seng Chan

Authors
  1. Jessica Soo Mee Wong
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  2. Chee Seng Chan
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Corresponding author

Correspondence to Jessica Soo Mee Wong .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, Nevada, USA

    Darko Koracin

  5. University of Houston, Houston, Texas, USA

    Ioannis Pavlidis

  6. IBM T.J. Watson Research Center, Yorktown Heights, New York, USA

    Rogerio Feris

  7. Purdue University, West Lafayette, Indiana, USA

    Tim McGraw

  8. Side Effects Software, Santa Monica, California, USA

    Mark Elendt

  9. The DiVE, Durham, North Carolina, USA

    Regis Kopper

  10. Texas A&M University, College Station, Texas, USA

    Eric Ragan

  11. Kent State University, Kent, Ohio, USA

    Zhao Ye

  12. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Gunther Weber

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Wong, J.S.M., Chan, C.S. (2015). Restoration of Blurred-Noisy Images Through the Concept of Bilevel Programming. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9475. Springer, Cham. https://doi.org/10.1007/978-3-319-27863-6_73

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  • DOI: https://doi.org/10.1007/978-3-319-27863-6_73

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27862-9

  • Online ISBN: 978-3-319-27863-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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