A machine learning framework for adaptive combination of signal denoising methods

David K. Hammond, Eero P. Simoncelli

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

Abstract

We present a general framework for combination of two distinct local denoising methods. Interpolation between the two methods is controlled by a spatially varying decision function. Assuming the availability of clean training data, we formulate a learning problem for determining the decision function. As an example application we use Weighted Kernel Ridge Regression to solve this learning problem for a pair of wavelet-based image denoising algorithms, yielding a "hybrid" denoising algorithm whose performance surpasses that of either initial method.

Original languageEnglish (US)
Title of host publication2007 IEEE International Conference on Image Processing, ICIP 2007 Proceedings
PagesVI29-VI32
DOIs
StatePublished - 2006
Event14th IEEE International Conference on Image Processing, ICIP 2007 - San Antonio, TX, United States
Duration: Sep 16 2007Sep 19 2007

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume6
ISSN (Print)1522-4880

Other

Other14th IEEE International Conference on Image Processing, ICIP 2007
CountryUnited States
CitySan Antonio, TX
Period9/16/079/19/07

Keywords

  • Image denoising
  • Image processing
  • Kernel Ridge Regression
  • Machine learning

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Hammond, D. K., & Simoncelli, E. P. (2006). A machine learning framework for adaptive combination of signal denoising methods. In 2007 IEEE International Conference on Image Processing, ICIP 2007 Proceedings (pp. VI29-VI32). [4379513] (Proceedings - International Conference on Image Processing, ICIP; Vol. 6). https://doi.org/10.1109/ICIP.2007.4379513