Journal article
Recovery of Discontinuous Signals Using Group Sparse Higher Degree Total Variation
IEEE signal processing letters, Vol.22(9), pp.1414-1418
09/2015
DOI: 10.1109/LSP.2015.2407321
Abstract
We introduce a family of novel regularization penalties to enable the recovery of discrete discontinuous piecewise polynomial signals from undersampled or degraded linear measurements. The penalties promote the group sparsity of the signal analyzed under a nth order derivative. We introduce an efficient alternating minimization algorithm to solve linear inverse problems regularized with the proposed penalties. Our experiments show that promoting group sparsity of derivatives enhances the compressed sensing recovery of discontinuous piecewise linear signals compared with an unstructured sparse prior. We also propose an extension to 2-D, which can be viewed as a group sparse version of higher degree total variation, and illustrate its effectiveness in denoising experiments.
Details
- Title: Subtitle
- Recovery of Discontinuous Signals Using Group Sparse Higher Degree Total Variation
- Creators
- Greg Ongie - Dept. of Math., Univ. of Iowa, Iowa City, IA, USAMathews Jacob - Dept. of Electr. & Comput. Eng., Univ. of Iowa, Iowa City, IA, USA
- Resource Type
- Journal article
- Publication Details
- IEEE signal processing letters, Vol.22(9), pp.1414-1418
- Publisher
- IEEE
- DOI
- 10.1109/LSP.2015.2407321
- ISSN
- 1070-9908
- eISSN
- 1558-2361
- Grant note
- 1R21HL109710-01A1 / National Institutes of Health (10.13039/100000002) RSG-11-267-01-CCE / American Cancer Society (10.13039/100000048) CCF- 1116067; CCF-0844812 / National Science Foundation (10.13039/100000001) N000141310202 / Office of Naval Research (10.13039/100000006)
- Language
- English
- Date published
- 09/2015
- Academic Unit
- Radiology; Iowa Neuroscience Institute; Electrical and Computer Engineering; Radiation Oncology; Roy J. Carver Department of Biomedical Engineering
- Record Identifier
- 9984070992602771
Metrics
13 Record Views