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Spatially Non-Uniform Blur Analysis Based on Wavelet Transform

Abstract Details

2010, Master of Science (M.S.), University of Dayton, Electrical Engineering.
Object motion causes spatially varying blur in an image. Partial blur typically carries useful information about the scene. This information is useful for consumer imaging as well as computer vision. However, spatially varying blur also deteriorates image quality. The goals of our research are finding out this information and making images better. In this thesis we introduce a novel method for solving this partial blur problem. We define a statistical model of a spatially-varying blur image and estimate the local point spread function (PSF) by using a set of methods including double wavelet transform and local autocorrelation. Experimental results demonstrate the effectiveness of the proposed algorithm
Keigo Hirakawa (Committee Chair)
Eric Balster (Committee Member)
Vijayan Asari (Committee Member)
38 p.

Recommended Citations

Citations

  • Zhang, Y. (2010). Spatially Non-Uniform Blur Analysis Based on Wavelet Transform [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1292527349

    APA Style (7th edition)

  • Zhang, Yi. Spatially Non-Uniform Blur Analysis Based on Wavelet Transform. 2010. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1292527349.

    MLA Style (8th edition)

  • Zhang, Yi. "Spatially Non-Uniform Blur Analysis Based on Wavelet Transform." Master's thesis, University of Dayton, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1292527349

    Chicago Manual of Style (17th edition)