Journal of Applied Mathematics

  • J. Appl. Math.
  • Volume 2012, Special Issue (2012), Article ID 467412, 16 pages.

Spatial Images Feature Extraction Based on Bayesian Nonlocal Means Filter and Improved Contourlet Transform

Pengcheng Han and Junping Du

Full-text: Open access

Abstract

Spatial images are inevitably mixed with different levels of noise and distortion. The contourlet transform can provide multidimensional sparse representations of images in a discrete domain. Because of its filter structure, the contourlet transform is not translation-invariant. In this paper, we use a nonsubsampled pyramid structure and a nonsubsampled directional filter to achieve multidimensional and translation-invariant image decomposition for spatial images. A nonsubsampled contourlet transform is used as the basis for an improved Bayesian nonlocal means (NLM) filter for different frequencies. The Bayesian model adds a sigma range in image a priori operations, which can be more effective in protecting image details. The NLM filter retains the image edge content and assigns greater weight to similarities for edge pixels. Experimental results both on standard images and spatial images confirm that the proposed algorithm yields significantly better performance than nonsubsampled wavelet transform, contourlet, and curvelet approaches.

Article information

Source
J. Appl. Math., Volume 2012, Special Issue (2012), Article ID 467412, 16 pages.

Dates
First available in Project Euclid: 3 January 2013

Permanent link to this document
https://projecteuclid.org/euclid.jam/1357178257

Digital Object Identifier
doi:10.1155/2012/467412

Mathematical Reviews number (MathSciNet)
MR2927284

Zentralblatt MATH identifier
1244.93157

Citation

Han, Pengcheng; Du, Junping. Spatial Images Feature Extraction Based on Bayesian Nonlocal Means Filter and Improved Contourlet Transform. J. Appl. Math. 2012, Special Issue (2012), Article ID 467412, 16 pages. doi:10.1155/2012/467412. https://projecteuclid.org/euclid.jam/1357178257


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