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Blind Image Deconvolution Using Machine Learning for Three-Dimensional Microscopy
December 2010 (vol. 32 no. 12)
pp. 2191-2204
Tal Kenig, Technion - Insitute of Technology, Haifa
Zvi Kam, Weizmann Institute of Science, Rehovot
Arie Feuer, Technion - Insitute of Technology, Haifa
In this work, we propose a novel method for the regularization of blind deconvolution algorithms. The proposed method employs example-based machine learning techniques for modeling the space of point spread functions. During an iterative blind deconvolution process, a prior term attracts the point spread function estimates to the learned point spread function space. We demonstrate the usage of this regularizer within a Bayesian blind deconvolution framework and also integrate into the latter a method for noise reduction, thus creating a complete blind deconvolution method. The application of the proposed algorithm is demonstrated on synthetic and real-world three-dimensional images acquired by a wide-field fluorescence microscope, where the need for blind deconvolution algorithms is indispensable, yielding excellent results.

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Index Terms:
Blind deconvolution, deblurring, machine learning, PCA, kernel PCA, microscopy.
Citation:
Tal Kenig, Zvi Kam, Arie Feuer, "Blind Image Deconvolution Using Machine Learning for Three-Dimensional Microscopy," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 12, pp. 2191-2204, Dec. 2010, doi:10.1109/TPAMI.2010.45
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