Nonlocal patch functional minimization for image denoising using nonsubsampled contourlet

Hui Wan*, Ran Tao

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

The nonsubsampled contour let transform (NSCT) is an excellent multi scale and multidirection representation for images, born in redundant and shift-invariant quality suitable for denoising. Most NSCT or NSCT-like denoising methods borrow the mature algorithms from wavelets, and are restricted by the precision of the prior model to describe the coefficients statistics. This paper presents a discrete regularization approach relying on the nonlocal weighted patches function and the NSCT sub band estimator, to relieve the effect of prior precision while suppressing the additive noise and the resultant artifacts. Results on the PSNR and vision comparisons with the advanced denoising algorithms demonstrate the superiority of the proposed method.

Original languageEnglish
Title of host publicationProceedings - 2011 International Conference on Instrumentation, Measurement, Computer, Communication and Control, IMCCC 2011
Pages740-743
Number of pages4
DOIs
Publication statusPublished - 2011
Event1st International Conference on Instrumentation and Measurement, Computer, Communication and Control, IMCCC2011 - Beijing, China
Duration: 21 Oct 201123 Oct 2011

Publication series

NameProceedings - 2011 International Conference on Instrumentation, Measurement, Computer, Communication and Control, IMCCC 2011

Conference

Conference1st International Conference on Instrumentation and Measurement, Computer, Communication and Control, IMCCC2011
Country/TerritoryChina
CityBeijing
Period21/10/1123/10/11

Keywords

  • Gaussian scale mixtures
  • image denoising
  • nonlocal means
  • nonsubsampled contourlet transform

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