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We describe a learning-based approach to blind image deconvolution. It uses a deep layered architecture, parts of which are borrowed from recent work on neural network learning, and parts of which incorporate computations that are specific to image deconvolution. The system is trained end-to-end on a set of artificially generated training examples, enabling competitive performance in blind deconvolution, both with respect to quality and runtime.
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Christian J. Schuler
Michael Hirsch
Stefan Harmeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Heinrich Heine University Düsseldorf
Max Planck Institute for Intelligent Systems
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Schuler et al. (Wed,) studied this question.
www.synapsesocial.com/papers/6a109edb4fb650da4fffbe89 — DOI: https://doi.org/10.1109/tpami.2015.2481418