Performance analysis of denoising with low-rank and sparsity constraints

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

Abstract

Recent denoising methods that exploit the low-rank property and sparsity of the underlying signals have produced impressive empirical results in various imaging applications. However, the fundamental limits of their denoising capability have not been systematically analyzed. This paper presents an analysis of the denoising effects of imposing low-rank and sparsity constraints. Specifically, we use the constrained Cramér-Rao lower bound to derive upper bounds on the maximum noise reduction when applying these two constraints, individually or simultaneously. We also perform numerical simulations to compare the theoretical bounds with noise reductions from practical denoising methods. These results should provide useful insights into the utility of low-rank and sparsity constraints for denoising.

Original languageEnglish (US)
Title of host publicationISBI 2013 - 2013 IEEE 10th International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro
Pages1223-1226
Number of pages4
DOIs
StatePublished - Aug 22 2013
Event2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013 - San Francisco, CA, United States
Duration: Apr 7 2013Apr 11 2013

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Other

Other2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013
CountryUnited States
CitySan Francisco, CA
Period4/7/134/11/13

Keywords

  • Cramér-Rao lower bound
  • Denoising
  • low-rank model
  • singular value decomposition
  • sparse representation

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Lam, F., Ma, C., & Liang, Z-P. (2013). Performance analysis of denoising with low-rank and sparsity constraints. In ISBI 2013 - 2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro (pp. 1223-1226). [6556701] (Proceedings - International Symposium on Biomedical Imaging). https://doi.org/10.1109/ISBI.2013.6556701