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Blind Image Deconvolution Using Machine Learning for Three-Dimensional Microscopy

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3 Author(s)
Tal Kenig ; Technion - Insitute of Technology, Haifa ; Zvi Kam ; Arie Feuer

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.

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:32 ,  Issue: 12 )