Bayesian multichannel image restoration using compound gauss-markov random fields

Molina, Rafael; Mateos, Javier; Katsaggelos, A.K.; Vega, Miguel

doi:10.1109/tip.2003.818015

Cited by 62 publications

(53 citation statements)

References 33 publications

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“…Supposing that the likelihood function can be expressed in Gibbs distribution, the MAP estimation becomes (4) where and are the likelihood and prior energy functions, respectively. This further leads to an energy minimization problem, i.e., (5) where is the solution to the segmentation problem. We assume that the intensity field and the boundary MRF are independent of each other because the observed image intensity is not affected whether the site is on the region boundary or inside the region.…”

Section: A Map-mrf Frameworkmentioning

confidence: 99%

“…Similar to the work of Geman and Geman [1], Geiger and Girosi [3] also added a second MRF (line process) to the original MRF for surface reconstruction. Likewise, in the work of Jeng and Woods [4] and Molina et al [5], line process (edge MRF) was incorporated into the intensity process (label MRF). In general, adopting two or more MRFs in one task is a way to solve two or more different problems.…”

Section: Introductionmentioning

confidence: 99%

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A Segmentation Model Using Compound Markov Random Fields Based on a Boundary Model

Chung

2007

IEEE Trans. on Image Process.

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Abstract-Markov random field (MRF) theory has been widely applied to the challenging problem of image segmentation. In this paper, we propose a new nontexture segmentation model using compound MRFs, in which the original label MRF is coupled with a new boundary MRF to help improve the segmentation performance. The boundary model is relatively general and does not need prior training on boundary patterns. Unlike some existing related work, the proposed method offers a more compact interaction between label and boundary MRFs. Furthermore, our boundary model systematically takes into account all the possible scenarios of a single edge existing in a 3 3 neighborhood and, thus, incorporates sophisticated prior information about the relation between label and boundary. It is experimentally shown that the proposed model can segment objects with complex boundaries and at the same time is able to work under noise corruption. The new method has been applied to medical image segmentation. Experiments on synthetic images and real clinical datasets show that the proposed model is able to produce more accurate segmentation results and satisfactorily keep the delicate boundary. It is also less sensitive to noise in both high and low signal-to-noise ratio regions than some of the existing models in common use.Index Terms-Boundary model, Markov random fields (MRFs), medical image segmentation.

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Section: A Map-mrf Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Segmentation Model Using Compound Markov Random Fields Based on a Boundary Model

Chung

2007

IEEE Trans. on Image Process.

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“…We want now, see [16] for details, to increase the probability of a new active line element in the position [(i, j), (u, v)] in a given channel if the other channels have a line in the same position.…”

Section: Multichannel Regularizationmentioning

confidence: 99%

“…Unfortunately, the use of the regularization term in Eq. (16) prevents us from using computationally efficient algorithms and other alternative algorithms have to be used, see [16] for details.…”

Section: Multichannel Regularizationmentioning

confidence: 99%

Multichannel Image Recovery

Galatsanos

Wernick

Katsaggelos³

et al. 2005

Handbook of Image and Video Processing

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“…To our knowledge, a lot of well known and popular existing HR reconstruction methods are often based on a regularized approach [5,6,7]. In this work, which is an extension of our previous work [1], we use a Bayesian estimation framework which gives the possibility to account for a broad range of prior models to formulate inverse problem of multi-frame SR restoration (we can mention for instance the previous works [8,9]). The innovation of the present study lies in the definition of region's homogeneity which follows a bilinear model here, instead of a constant one in [1].…”

Section: Introductionmentioning

confidence: 99%

Super-Resolution and Joint Segmentation in Bayesian Framework

Humblot¹

2005

AIP Conference Proceedings

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Abstract.This communication presents an extension to a super-resolution (SR) method we previously exposed in [1]. SR techniques involve several low-resolution (LR) images in the reconstruction's process of a high-resolution (HR) image. The LR images are assumed to be obtained from the HR image through optical and sensor blurs, shift movement and decimation operators, and finally corruption by a random noise. Moreover, the HR image is assumed to be composed of a finite number of homogeneous regions. Thus, we associate to each pixel of the HR image a classification variable which is modeled by a Potts Markov field. The SR problem is then expressed as a Bayesian joint estimation of the HR image pixel values, its classification labels variable, and the problem's hyperparameters. These estimations are performed using an appropriate algorithm based on hybrid Markov Chain Monte-Carlo (MCMC) Gibbs sampling.In this study, we distinguish two kinds of region's homogeneity: the first one follows a constant model, and the second a bilinear model. Our previous work [1] only deals with constant model. Finally we conclude this work showing simulation results obtained with synthetic and real data.

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Bayesian multichannel image restoration using compound gauss-markov random fields

Cited by 62 publications

References 33 publications

A Segmentation Model Using Compound Markov Random Fields Based on a Boundary Model

A Segmentation Model Using Compound Markov Random Fields Based on a Boundary Model

Multichannel Image Recovery

Super-Resolution and Joint Segmentation in Bayesian Framework

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