Convolutional Dictionary Learning: A Comparative Review and New Algorithms

Garcia-Cardona, Cristina; Wohlberg, Brendt

doi:10.1109/tci.2018.2840334

Convolutional Dictionary Learning: A Comparative Review and New Algorithms

Journal Article · Sat Sep 01 00:00:00 EDT 2018 · IEEE Transactions on Computational Imaging

DOI:https://doi.org/10.1109/tci.2018.2840334· OSTI ID:1711366

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Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Convolutional sparse representations are a form of sparse representation with a dictionary that has a structure that is equivalent to convolution with a set of linear filters. Additionally, while effective algorithms have recently been developed for the convolutional sparse coding problem, the corresponding dictionary learning problem is substantially more challenging. Furthermore, although a number of different approaches have been proposed, the absence of thorough comparisons between them makes it difficult to determine which of them represents the current state of the art. The present work both addresses this deficiency and proposes some new approaches that outperform existing ones in certain contexts. A thorough set of performance comparisons indicates a very wide range of performance differences among the existing and proposed methods, and clearly identifies those that are the most effective.

View Accepted Manuscript (DOE)

Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 1711366

Report Number(s):: LA-UR--17-27612

Journal Information:: IEEE Transactions on Computational Imaging, Journal Name: IEEE Transactions on Computational Imaging Journal Issue: 3 Vol. 4; ISSN 2573-0436

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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Cited By (6)

Convolutional Transform Learning Maggu, Jyoti; Chouzenoux, Emilie; Chierchia, Giovanni Neural Information Processing https://doi.org/10.1007/978-3-030-04182-3_15	book	January 2018
Learning Filter Bank Sparsifying Transforms Pfister, Luke; Bresler, Yoram IEEE Transactions on Signal Processing, Vol. 67, Issue 2 https://doi.org/10.1109/tsp.2018.2883021	journal	January 2019
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