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Computationally Efficient Video Restoration for Nyquist Sampled Imaging Sensors Combining an Affine-Motion Based Temporal Kalman Filter and Adaptive Wiener Filter

Rucci, Michael
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1398286798

Abstract Details

2014, Master of Science (M.S.), University of Dayton, Electrical Engineering.
In this thesis we present a computationally efficient video restoration algorithm to address both blur and noise for a Nyquist sampled imaging system. The proposed method utilizes a temporal Kalman filter followed by a correlation-model based spatial adaptive wiener filter (AWF). The Kalman filter employs an affine background motion model and novel process-noise variance estimate. We also propose and demonstrate a new multi-delay temporal Kalman filter designed to more robustly treat local motion. The AWF is a spatial operation that performs deconvolution and adapts to the spatially-varying residual noise left in the Kalman filter stage. In image areas where the temporal Kalman filter is able to provide significant noise reduction, the AWF can be aggressive in its deconvolution. In other areas, where less noise reduction is achieved with the Kalman filter, the AWF balances the deconvolution with spatial noise reduction. In this way, the Kalman filter and AWF work together effectively, but without the computational burden of full joint spatio-temporal processing. We also propose a novel hybrid system that combines a temporal Kalman filter and BM3D processing. To illustrate the efficacy of the proposed methods, we test the algorithms on both simulated imagery and video collected with a visible camera.
Russell Hardie, Ph.D. (Advisor)
Kenneth Barnard, Ph.D. (Committee Member)
Eric Balster, Ph.D. (Committee Member)
Electrical Engineering
Digital Image Processing, Image Reconstruction-Restoration, Deconvolution

Recommended Citations

Citations

  • Rucci, M. (2014). Computationally Efficient Video Restoration for Nyquist Sampled Imaging Sensors Combining an Affine-Motion Based Temporal Kalman Filter and Adaptive Wiener Filter [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1398286798

    APA Style (7th edition)

  • Rucci, Michael. Computationally Efficient Video Restoration for Nyquist Sampled Imaging Sensors Combining an Affine-Motion Based Temporal Kalman Filter and Adaptive Wiener Filter. 2014. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1398286798.

    MLA Style (8th edition)

  • Rucci, Michael. "Computationally Efficient Video Restoration for Nyquist Sampled Imaging Sensors Combining an Affine-Motion Based Temporal Kalman Filter and Adaptive Wiener Filter." Master's thesis, University of Dayton, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1398286798

    Chicago Manual of Style (17th edition)

dayton1398286798
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This open access ETD is published by University of Dayton and OhioLINK.