Cross-scale predictive dictionaries for image and video restoration

Conference Paper

We propose a novel signal model, based on sparse representations, that captures cross-scale features for visual signals. We show that cross-scale predictive model enables faster solutions to sparse approximation problems. This is achieved by first solving the sparse approximation problem for the downsampled signal and using the support of the solution to constrain the support at the original resolution. The speedups obtained are especially compelling for high-dimensional signals that require large dictionaries to provide precise sparse approximations. We demonstrate speedups in the order of 10-20x for denoising and up to 9x speed-ups for compressive sensing of images and videos.

Full Text

Duke Authors

Cited Authors

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

Published Date

  • August 3, 2016

Published In

Volume / Issue

  • 2016-August /

Start / End Page

  • 709 - 713

International Standard Serial Number (ISSN)

  • 1522-4880

International Standard Book Number 13 (ISBN-13)

  • 9781467399616

Digital Object Identifier (DOI)

  • 10.1109/ICIP.2016.7532449

Citation Source

  • Scopus