Oscillation regularization

Published

Journal Article

We measure the degree of oscillation of a sampled function f by the number of its local extrema. The greater this number, the more oscillatory and complex f becomes. In signal denoising, we want a restored function g that is simple and fits the data f well. We propose to model this by a global optimization, coined oscillation regularization, that reduces both the data fitting error and the number of local extrema of g: equation where err(f, g) measures the discrepancy between f and g and λ is a regularization parameter. To the best of our knowledge, the number of local extrema of g is a topological prior that is rarely exploited in the literature of regularization. © 2012 IEEE.

Full Text

Duke Authors

Cited Authors

  • Gu, S; Zheng, Y; Tomasi, C

Published Date

  • October 23, 2012

Published In

Start / End Page

  • 3837 - 3840

International Standard Serial Number (ISSN)

  • 1520-6149

Digital Object Identifier (DOI)

  • 10.1109/ICASSP.2012.6288754

Citation Source

  • Scopus