Fast convolutional sparse coding using matrix inversion lemma

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F16%3A00459332" target="_blank" >RIV/67985556:_____/16:00459332 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1016/j.dsp.2016.04.012" target="_blank" >http://dx.doi.org/10.1016/j.dsp.2016.04.012</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.dsp.2016.04.012" target="_blank" >10.1016/j.dsp.2016.04.012</a>

Result language
angličtina
Original language name
Fast convolutional sparse coding using matrix inversion lemma
Original language description
Convolutional sparse coding is an interesting alternative to standard sparse coding in modeling shift-invariant signals, giving impressive results for example in unsupervised learning of visual features. In state-of-the-art methods, the most time-consuming parts include inversion of a linear operator related to convolution. In this article we show how these inversions can be computed non-iteratively in the Fourier domain using the matrix inversion lemma. This greatly speeds up computation and makes convolutional sparse coding computationally feasible even for large problems. The algorithm is derived in three variants, one of them especially suitable for parallel implementation. We demonstrate algorithms on two-dimensional image data but all results hold for signals of arbitrary dimension.
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
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Project
<a href="/en/project/GA13-29225S" target="_blank" >GA13-29225S: Image Blind Deconvolution in Demanding Conditions</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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