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Conference Paper

Multiframe Blind Deconvolution, Super-Resolution, and Saturation Correction via Incremental EM

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;

/persons/resource/persons76142

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

/persons/resource/persons83969

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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https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5651650
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Citation

Harmeling, S., Sra, S., Hirsch, M., & Schölkopf, B. (2010). Multiframe Blind Deconvolution, Super-Resolution, and Saturation Correction via Incremental EM. In 17th International Conference on Image Processing (ICIP 2010) (pp. 3313-3316). Piscataway, NJ, USA: IEEE.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-BE6E-D
Abstract
We formulate the multiframe blind deconvolution problem in an incremental
expectation maximization (EM) framework. Beyond deconvolution,
we show how to use the same framework to address: (i)
super-resolution despite noise and unknown blurring; (ii) saturationcorrection
of overexposed pixels that confound image restoration.
The abundance of data allows us to address both of these without
using explicit image or blur priors. The end result is a simple but effective
algorithm with no hyperparameters. We apply this algorithm
to real-world images from astronomy and to super resolution tasks:
for both, our algorithm yields increased resolution and deconvolved
images simultaneously.