Cross-Scale Predictive Dictionaries.

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Saragadam, V; Li, X; Sankaranarayanan, AC

Published Date

  • February 2019

Published In

Volume / Issue

  • 28 / 2

Start / End Page

  • 803 - 814

PubMed ID

  • 30222567

Pubmed Central ID

  • 30222567

Electronic International Standard Serial Number (EISSN)

  • 1941-0042

International Standard Serial Number (ISSN)

  • 1057-7149

Digital Object Identifier (DOI)

  • 10.1109/tip.2018.2869719

Language

  • eng