Non-local patch regression: Robust image denoising in patch space

Kunal N. Chaudhury, Amit Singer

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

13 Scopus citations

Abstract

It was recently demonstrated in [13] that the denoising performance of Non-Local Means (NLM) can be improved at large noise levels by replacing the mean by the robust Euclidean median. Numerical experiments on synthetic and natural images showed that the latter consistently performed better than NLM beyond a certain noise level, and significantly so for images with sharp edges. The Euclidean mean and median can be put into a common regression (on the patch space) framework, in which the ℓ2 norm of the residuals is considered in the former, while the ℓ1 norm is considered in the latter. The natural question then is what happens if we consider ℓp (0 < p < 1) regression? We investigate this possibility in this paper.

Original languageEnglish (US)
Title of host publication2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
Pages1345-1349
Number of pages5
DOIs
StatePublished - Oct 18 2013
Event2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada
Duration: May 26 2013May 31 2013

Publication series

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

Other

Other2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
CountryCanada
CityVancouver, BC
Period5/26/135/31/13

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Keywords

  • Image denoising
  • edges
  • inlier-outlier model
  • iteratively reweighted least squares
  • non-convex optimization
  • non-local Euclidean medians
  • non-local means
  • robustness
  • sparsity

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  • Cite this

    Chaudhury, K. N., & Singer, A. (2013). Non-local patch regression: Robust image denoising in patch space. In 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings (pp. 1345-1349). [6637870] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2013.6637870