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Convolutional dictionary learning is a significant approach to train dictionaries for inverse problems in image processing. However, for large traing sets, state-of-the-art solvers are not efficient since their large time and memory costs. To fix this problem, we propose several algorithms for convolutional dictionary learning in the framework of distributed computing and online computing, which are efficient approaches for large-scale data sets. Simulation results of these methods are given and compared. Host: Chris Neale |