期刊
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
卷 38, 期 7, 页码 -出版社
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2015.2481418
关键词
Sharpening and deblurring; neural networks; machine learning
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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