Learning a wavelet tree for multichannel image denoising

Zhen James Xiang, Zhuo Zhang, Pingmei Xu, Peter J. Ramadge

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

1 Scopus citations

Abstract

We propose a new multichannel image denoising algorithm. To exploit important inter-channel dependencies, we first use dynamic programming to learn an explicit dyadic tree representation of the common structure of the channels. Based on this dyadic tree, optimal Haar wavelet thresholding is then applied to denoise the image. In addition to the original channels, the algorithm can employ multiple derived channels to improve tree learning. Experimental results confirm that the approach improves multichannel image denoising performance both in PSNR and in edge preservation.

Original languageEnglish (US)
Title of host publicationICIP 2011
Subtitle of host publication2011 18th IEEE International Conference on Image Processing
Pages2565-2568
Number of pages4
DOIs
StatePublished - Dec 1 2011
Event2011 18th IEEE International Conference on Image Processing, ICIP 2011 - Brussels, Belgium
Duration: Sep 11 2011Sep 14 2011

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Other

Other2011 18th IEEE International Conference on Image Processing, ICIP 2011
CountryBelgium
CityBrussels
Period9/11/119/14/11

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

Keywords

  • Wavelet transforms
  • dynamic programming
  • image enhancement
  • signal denoising

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