Denoising sparse noise via online dictionary learning

A. Cherian, S. Sra, Nikolaos P Papanikolopoulos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

The idea of learning overcomplete dictionaries based on the paradigm of compressive sensing has found numerous applications, among which image denoising is considered one of the most successful. But many state-of-the-art denoising techniques inherently assume that the signal noise is Gaussian. We instead propose to learn overcomplete dictionaries where the signal is allowed to have both Gaussian and (sparse) Laplacian noise. Dictionary learning in this setting leads to a difficult non-convex optimization problem, which is further exacerbated by large input datasets. We tackle these difficulties by developing an efficient online algorithm that scales to data size. To assess the efficacy of our model, we apply it to dictionary learning for data that naturally satisfy our noise model, namely, Scale Invariant Feature Transform (SIFT) descriptors. For these data, we measure performance of the learned dictionary on the task of nearest-neighbor retrieval: compared to methods that do not explicitly model sparse noise our method exhibits superior performance.

Original languageEnglish (US)
Title of host publication2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Pages2060-2063
Number of pages4
DOIs
StatePublished - Aug 18 2011
Event36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic
Duration: May 22 2011May 27 2011

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
CountryCzech Republic
CityPrague
Period5/22/115/27/11

Keywords

  • denoising
  • dictionary learning
  • sparsity

Fingerprint Dive into the research topics of 'Denoising sparse noise via online dictionary learning'. Together they form a unique fingerprint.

Cite this