Issue No. 12 - December (2010 vol. 32)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2010.45
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.
Blind deconvolution, deblurring, machine learning, PCA, kernel PCA, microscopy.
Z. Kam, T. Kenig and A. Feuer, "Blind Image Deconvolution Using Machine Learning for Three-Dimensional Microscopy," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 32, no. , pp. 2191-2204, 2010.