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A Collaborative Adaptive Wiener Filter for Image Restoration and Multi-frame Super-resolution

Mohamed, Khaled Mohamed Ahmied
http://orcid.org/0000-0001-9741-2607
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1426536832

Abstract Details

2015, Doctor of Philosophy (Ph.D.), University of Dayton, Engineering.
In this dissertation, we have presented a novel patch-based algorithm using an adaptive Wiener filter (AWF) for image restoration as well as for multi-frame super-resolution (SR). The new filter structure is referred to as a collaborative adaptive Wiener filter (CAWF), which can be thought of an extension of the AWF using multiple patches. Starting with CAWF for image restoration, in each reference window, the most similar patches are identified. The output is formed as a single-pass weighted sum of all of the pixels from the multiple selected patches. Wiener weights which are derived from a novel spatial-domain multi-patch correlation model, are used to provide a minimum mean square error (MSE) estimate for this filter structure. While the first step of the CAWF for multi-frame super-resolution (CAWF SR) method is to register all low-resolution (LR) frames one a common high-resolution (HR) grid. Then, for each LR pixel position on the HR grid, we also identify the most similar patches within a given search window about the reference patch. The output HR pixels in the estimate window at the reference patch are obtained using a weighted sum of all of LR pixels of the selected similar patches. The weights are based on a new nonuniformly sampled multi-patch correlation model. A key aspect of the CAWF method for both restoration and multi-frame SR is the new spatial-domain multi-patch correlation model. This model attempts to capture the spatial correlation among the samples within a given patch, and also the correlations among the patches. The CAWF is able to jointly perform nonuniform interpolation, denoising and deblurring. We believe this type of joint restoration and interpolation is advantageous, compared with decoupling these operations. The CAWF algorithm is also capable of adapting to local signal and noise variance. Bad or missing pixels can easily be accommodated by leaving them out of the multi-patch observation vector and corresponding correlation statistics. We provide several experiments on the CAWF for image restoration and multi-frame SR using simulated and real data. The experimental results presented show that the proposed method delivers high performance in image restoration as well as in multi-frame SR.
Ressull Hardie (Advisor)
85 p.
Engineering
Image restoration; Wiener filter; correlation model; patch-based; multi-frame; image super-resolution

Recommended Citations

Citations

  • Mohamed, K. M. A. (2015). A Collaborative Adaptive Wiener Filter for Image Restoration and Multi-frame Super-resolution [Doctoral dissertation, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1426536832

    APA Style (7th edition)

  • Mohamed, Khaled. A Collaborative Adaptive Wiener Filter for Image Restoration and Multi-frame Super-resolution . 2015. University of Dayton, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1426536832.

    MLA Style (8th edition)

  • Mohamed, Khaled. "A Collaborative Adaptive Wiener Filter for Image Restoration and Multi-frame Super-resolution ." Doctoral dissertation, University of Dayton, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1426536832

    Chicago Manual of Style (17th edition)

dayton1426536832
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© 2015, all rights reserved.
This open access ETD is published by University of Dayton and OhioLINK.