Spatial images feature extraction based on Bayesian nonlocal means filter and improved contourlet transform
type: Item
instance of: scholarly article
title: Spatial images feature extraction based on Bayesian nonlocal means filter and improved contourlet transform
zbMATH Open document ID: 1244.93157
DOI: 10.1155/2012/467412
author: Pengcheng Han
author: Junping Du
published in: Journal of Applied Mathematics
publication date: 2012-08-06T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 93E11
Mathematics Subject Classification ID: 94A12
zbMATH DE Number: 6063398
describes a project that uses: NSCT toolbox
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2012/467412
OpenAlex ID: W2062286073
Wikidata QID: Q58905926
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description: scientific article; zbMATH DE number 6063398