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A Stochastic Method for Face Image Super-Resolution

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Advances in Visual Computing (ISVC 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5875))

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Abstract

In surveillance applications, cameras are usually set up with wide fields of view to capture as much of the scene as possible. This normally results in low-resolution images of the objects of interest. Since most image analysis applications require high or medium resolution inputs, the development of approaches aiming at improving the quality of these image regions has been an active research area in the last few years. A new family of approaches, based on statistical machine learning, aims at analyzing large data sets of images of a particular class of objects and learning the mapping from low-quality to high-quality images of that class. This enables them to infer, for example, the most likely high-resolution face image depicting the same person as a low-resolution image given as input. These super-resolution algorithms are time-consuming, due to the need for exhaustive search in a database of models. This work improves the efficiency of face image super-resolution using stochastic search for local modeling. Experimental results show that the proposed algorithm generates high-quality face images from low-resolution inputs while reducing the computation time dramatically.

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Author information

Authors and Affiliations

  1. Computer Science Department, University of Texas at El Paso, El Paso, Texas, 79968, U.S.A.

    Jun Zheng & Olac Fuentes

Authors
  1. Jun Zheng
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  2. Olac Fuentes
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, Reno, USA

    George Bebis

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

    Richard Boyle

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

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Graduate School of Science and Engineering, Saitama University, 255 Shimo-Okubo, Sakura-ku, Saitama-shi, 338-8570, Saitama, Japan

    Yoshinori Kuno

  6. Institute for Infocomm Research, 21 Heng Mui Keng Terrace, P.O. Box, 119613, Singapore

    Junxian Wang

  7. Department of Computer Science & Information Engineering, Tamkang University, Tamsui, Taipei, Taiwan, R.O.C.

    Jun-Xuan Wang

  8. Microsoft Research, Redmond, WA, USA

    Junxian Wang

  9. Department of Informatics, Univ. of Zurich, Winterthurerstr. 190, P.O. Box, 8057, Zurich, Switzerland

    Renato Pajarola

  10. Lawrence Livermore National Laboratory, 94550, Livermore, CA, USA

    Peter Lindstrom

  11. University of Applied Sciences Bonn-Rhein-Sieg, 53754, Sankt Augustin, Germany

    André Hinkenjann

  12. Humana Inc., 40202, Louisville, KY, USA

    Miguel L. Encarnação

  13. SCI Institute & School of Computing, University of Utah, 84112, Salt Lake City, UT, USA

    Cláudio T. Silva

  14. Desert Research Institute, 89512, Reno, NV, USA

    Daniel Coming

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© 2009 Springer-Verlag Berlin Heidelberg

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Zheng, J., Fuentes, O. (2009). A Stochastic Method for Face Image Super-Resolution. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2009. Lecture Notes in Computer Science, vol 5875. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10331-5_71

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  • DOI: https://doi.org/10.1007/978-3-642-10331-5_71

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10330-8

  • Online ISBN: 978-3-642-10331-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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