Denoising of multicomponent images using wavelet least-squares estimators

Backer, S. De; Piurica, Aleksandra; Huysmans, Bruno; Philips, Wilfried; Scheunders, Paul

doi:10.1016/j.imavis.2007.11.003

Cited by 24 publications

(25 citation statements)

References 44 publications

(86 reference statements)

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“…In the latter work, with the aid of the KLD, good retrieval rates were obtained, but a computationally complex gaussianization step was required. In addition, the process of wavelet-based multivariate image denoising has been treated recently by several authors (Benazza-Benyahia and Pesquet, 2005;Pižurica and Philips, 2006;De Backer et al, 2008). In these works, multivariate probability density functions of the images were proposed that account for the correlations between the image bands.…”

Section: Introductionmentioning

confidence: 99%

Geodesics on the Manifold of Multivariate Generalized Gaussian Distributions with an Application to Multicomponent Texture Discrimination

Verdoolaege

Scheunders

2011

Int J Comput Vis

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We consider the Rao geodesic distance (GD) based on the Fisher information as a similarity measure on the manifold of zero-mean multivariate generalized Gaussian distributions (MGGD). The MGGD is shown to be an adequate model for the heavy-tailed wavelet statistics in multicomponent images, such as color or multispectral images. We discuss the estimation of MGGD parameters using various methods. We apply the GD between MGGDs to color texture discrimination in several classification experiments, taking into account the correlation structure between the spectral bands in the wavelet domain. We compare the performance, both in terms of texture discrimination capability and computational load, of the GD and the KullbackLeibler divergence (KLD). Likewise, both uni-and multivariate generalized Gaussian models are evaluated, characterized by a fixed or a variable shape parameter. The modeling of the interband correlation significantly improves classification efficiency, while the GD is shown to consistently outperform the KLD as a similarity measure.

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Section: Introductionmentioning

confidence: 99%

Geodesics on the Manifold of Multivariate Generalized Gaussian Distributions with an Application to Multicomponent Texture Discrimination

Verdoolaege

Scheunders

2011

Int J Comput Vis

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“…Torres et al [9] adopted wavelet transform to data recovery on multispectral images. Baker et al [10] also proposed an improved wavelet analysis algorithm to deal with noise on multispectral images. but the algorithm had certain defects, such as data recovery was not complete, introduced new data loss, and so on.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Remote Sensing Data Recovery via Adaptive Window Matching Method

Zeng¹,

Lan²,

Han³

et al. 2015

Int. J. Onl. Eng.

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Abstract-HJ-1A satellite is often used to monitor environmental disaster and plays an important role in environmental changes. Because of the affection of various factors, certain band of HJ-1A hyperspectral remote sensing data is severe loss or distortion, which brings great difficulties for subsequent quantitative processing. A novel adaptive window matching algorithm, which can adjust intelligently size of matching window according to different local feature information of the image, is proposed for HJ-1A satellite hyperspectral data recovery in this paper. The results show that the adaptive matching algorithm has a more superior performance than other algorithms in image quality index, column mean curve, and image correlation coefficient.

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“…This fact motivated the development of many nonlinear methods that try to overcome this drawback. Within the nonlinear methods, we can find families of filters based on different approaches, such as weighted averaging [3,4,5], peer group averaging [6], fuzzy logic or soft switching [7,8,9,10], regularization filters [11], and wavelet filtering [12,13].…”

Section: Introductionmentioning

confidence: 99%

Colour image smoothing through a soft-switching mechanism using a graph model

Jordán

Morillas

Codesal

2012

IET Image Processing

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In this paper we propose a soft-switching filter to improve the performance of recent colour image smoothing filters when processing homogeneous image regions. We use a recent filter mixed with the classical arithmetic mean filter (AMF). The recent method is used to process image pixels close to edges, texture and details and the AMF is only used to process homogeneous regions. To this end, we propose a method based on graph theory to distinguish image details and homogeneous regions and to perform a soft-switching between the two filters. Experimental results show that the proposed method provides improved results which supports the * The authors acknowledge the support of Spanish Ministry of Science and Innovation under Grant MTM2009-12872-C02-01, Spanish Ministry of Science and Technology under grant MTM2010-18539 and DGCYT under grant MTM2009-08933 1 appropriateness of the graph theory-based method and suggests that the same structure can be used to improve the performance of other nonlinear colour image smoothing methods.

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Denoising of multicomponent images using wavelet least-squares estimators

Cited by 24 publications

References 44 publications

Geodesics on the Manifold of Multivariate Generalized Gaussian Distributions with an Application to Multicomponent Texture Discrimination

Geodesics on the Manifold of Multivariate Generalized Gaussian Distributions with an Application to Multicomponent Texture Discrimination

Hyperspectral Remote Sensing Data Recovery via Adaptive Window Matching Method

Colour image smoothing through a soft-switching mechanism using a graph model

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