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231225s2019 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2018.2869719
|2 doi
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|a eng
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| 100 |
1 |
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|a Saragadam, Vishwanath
|e verfasserin
|4 aut
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| 245 |
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|a Cross-Scale Predictive Dictionaries
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|c 2019
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 12.10.2018
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|a Date Revised 12.10.2018
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Sparse representations using data dictionaries provide an efficient model particularly for signals that do not enjoy alternate analytic sparsifying transformations. However, solving inverse problems with sparsifying dictionaries can be computationally expensive, especially when the dictionary under consideration has a large number of atoms. In this paper, we incorporate additional structure on to dictionary-based sparse representations for visual signals to enable speedups when solving sparse approximation problems. The specific structure that we endow onto sparse models is that of a multi-scale modeling where the sparse representation at each scale is constrained by the sparse representation at coarser scales. We show that this cross-scale predictive model delivers significant speedups, often in the range of , with little loss in accuracy for linear inverse problems associated with images, videos, and light fields
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|a Journal Article
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| 700 |
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|a Li, Xin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Sankaranarayanan, Aswin C
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 28(2019), 2 vom: 12. Feb., Seite 803-814
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnas
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| 773 |
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|g volume:28
|g year:2019
|g number:2
|g day:12
|g month:02
|g pages:803-814
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|u http://dx.doi.org/10.1109/TIP.2018.2869719
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