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An Incremental GEM Framework for Multiframe Blind Deconvolution, Super-Resolution, and Saturation Correction

MPS-Authors
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Harmeling,  S
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Sra,  S
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Hirsch,  M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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obd_paper_mpitechreport_6327[0].pdf
(Publisher version), 3MB

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Citation

Harmeling, S., Sra, S., Hirsch, M., & Schölkopf, B.(2009). An Incremental GEM Framework for Multiframe Blind Deconvolution, Super-Resolution, and Saturation Correction (187). Tübingen, Germany: Max Planck Institute for Biological Cybernetics.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-C218-1
Abstract
We develop an incremental generalized expectation maximization (GEM) framework to model the multiframe blind deconvolution problem. A simplistic version of this problem was recently studied by Harmeling etal~citeharmeling09}. We solve a more realistic version of this problem which includes the following major features: (i) super-resolution ability emph{despite noise and unknown blurring; (ii) saturation-correction, i.e., handling of overexposed pixels that can otherwise confound the image processing; and (iii) simultaneous handling of color channels. These features are seamlessly integrated into our incremental GEM framework to yield simple but efficient multiframe blind deconvolution algorithms. We present technical details concerning critical steps of our algorithms, especially to highlight how all operations can be written using matrix-vector multiplications. We apply our algorithm to real-world images from astronomy and super resolution tasks. Our experimental results show that our methods yield improve d resolution and deconvolution at the same time.