Paper Title:
Color Image Denoising Using Gaussian Multiscale Multivariate Image Analysis
  Abstract

Inspired by the human vision system, a new image representation and analysis model based on Gaussian multiscale multivariate image analysis (MIA) is proposed. The multiscale color texture representations for the original image are used to constitute the multivariate image, each channel of which represents a perceptual observation from different scales. Then the MIA decomposes this multivariate image into multiscale color texture perceptual features (the principal component score images). These score images could be interpreted as 1) the output of three color opponent channels: black versus white, red versus green and blue versus yellow, and 2) the edge information, and 3) higher-order Gaussian derivatives. Finally the color image denoising approach based on the models is presented. Experiments show that this denoising method against Gaussian filters significantly improves the denoising effect by preserving more edge information.

  Info
Periodical
Edited by
Yi-Min Deng, Aibing Yu, Weihua Li and Di Zheng
Pages
248-252
DOI
10.4028/www.scientific.net/AMM.37-38.248
Citation
D. T. Liang, "Color Image Denoising Using Gaussian Multiscale Multivariate Image Analysis", Applied Mechanics and Materials, Vols. 37-38, pp. 248-252, 2010
Online since
November 2010
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