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Convolutional dictionary learning (CDL) estimates shift invariant basisadapted to multidimensional data. CDL has proven useful for image denoising orinpainting, as well as for pattern discovery on multivariate signals. Asestimated patterns can be positioned anywhere in signals or images,optimization techniques face the difficulty of working in extremely highdimensions with millions of pixels or time samples, contrarily to standardpatch-based dictionary learning. To address this optimization problem, thiswork proposes a distributed and asynchronous algorithm, employing locallygreedy coordinate descent and an asynchronous locking mechanism that does notrequire a central server. This algorithm can be used to distribute thecomputation on a number of workers which scales linearly with the encodedsignal's size. Experiments confirm the scaling properties which allows us tolearn patterns on large scales images from the Hubble Space Telescope.

Distributed Convolutional Dictionary Learning (DiCoDiLe): Pattern Discovery in Large Images and Signals