Joint NDT Image Restoration and Segmentation Using Gauss–Markov–Potts Prior Models and Variational Bayesian Computation

Ayasso, Hacheme; Mohammad-Djafari, Ali

doi:10.1109/tip.2010.2047902

Cited by 75 publications

(68 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For each of these prior models, we discuss their properties and the way to use them in a Bayesian approach resulting to many different inversion algorithms. We have applied these Bayesian algorithms in many different applications such as X-ray computed tomography [35,36], optical diffraction tomography [37][38][39], positron emission tomography [40], Microwave imaging [41,42], Sources separation [43][44][45][46], spectrometry [47,48], Hyper spectral imaging [49], super resolution [50][51][52], image fusion [53], image segmentation [54], synthetic aperture radar (SAR) imaging [29]. To save the place and be very synthetic, we did not give here any simulation results or any results on different applications of these methods.…”

Section: Resultsmentioning

confidence: 99%

Bayesian approach with prior models which enforce sparsity in signal and image processing

Mohammad-Djafari

2012

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

In this review article, we propose to use the Bayesian inference approach for inverse problems in signal and image processing, where we want to infer on sparse signals or images. The sparsity may be directly on the original space or in a transformed space. Here, we consider it directly on the original space (impulsive signals). To enforce the sparsity, we consider the probabilistic models and try to give an exhaustive list of such prior models and try to classify them. These models are either heavy tailed (generalized Gaussian, symmetric Weibull, Student-t or Cauchy, elastic net, generalized hyperbolic and Dirichlet) or mixture models (mixture of Gaussians, Bernoulli-Gaussian, Bernoulli-Gamma, mixture of translated Gaussians, mixture of multinomial, etc.). Depending on the prior model selected, the Bayesian computations (optimization for the joint maximum a posteriori (MAP) estimate or MCMC or variational Bayes approximations (VBA) for posterior means (PM) or complete density estimation) may become more complex. We propose these models, discuss on different possible Bayesian estimators, drive the corresponding appropriate algorithms, and discuss on their corresponding relative complexities and performances.

show abstract

Section: Resultsmentioning

confidence: 99%

Bayesian approach with prior models which enforce sparsity in signal and image processing

Mohammad-Djafari

2012

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

show abstract

“…where X n = {x | σ(x) = n} is the set of pixels belonging to the nth region andp Xn f (v) is a histogram formed from the pixels in X n as defined in (3). This optimization finds a labeling function, σ, that splits the image into N regions.…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…Applications of image segmentation are numerous: from remote sensing [1] and video processing [2] to non-destructive testing [3]. Within the biomedical field, segmentation is used with diverse imaging modalities such as MRI [4], both light microscopy [5]- [7] and electron microscopy [8], ultrasound [9], and many others to identify regions at all scales from organelles to organisms.…”

Section: Introductionmentioning

confidence: 99%

Images as Occlusions of Textures: A Framework for Segmentation

McCann

Mixon

Fickus

et al. 2014

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

Abstract-We propose a new mathematical and algorithmic framework for unsupervised image segmentation, which is a critical step in a wide variety of image processing applications. We have found that most existing segmentation methods are not successful on histopathology images, which prompted us to investigate segmentation of a broader class of images, namely those without clear edges between the regions to be segmented. We model these images as occlusions of random images, which we call textures, and show that local histograms are a useful tool for segmenting them. Based on our theoretical results, we describe a flexible segmentation framework that draws on existing work on non-negative matrix factorization and image deconvolution. Results on synthetic texture mosaics and real histology images show the promise of the method.

show abstract

“…Interesting works [17][18][19][20][21][22][23] are the Bayesian methods for image segmentation from indirect data (inversionsegmentation) also based a Potts model for the labels. Nevertheless, the existing developments are not adapted for textures.…”

Section: Introductionmentioning

confidence: 99%

Deconvolution-segmentation for textured images

Giovannelli

Vacar

2017

2017 25th European Signal Processing Conference (EUSIPCO)

View full text Add to dashboard Cite

The paper tackles the problem of joint deconvolution and segmentation specifically for textured images. The images are composed of patches of textures that belong to a set of K possible classes. Each class of image is described by a Gaussian random field and the classes are modelled by a Potts field. The method relies on a hierarchical model and a Bayesian strategy to jointly estimate the labels, the textured images as well as the hyperparameters. An important point is that the parameter of the Potts field is also estimated. The estimators are designed in an optimal manner (marginal posterior maximizer for the labels and posterior mean for the other unknowns). They are computed based on a convergent procedure, from samples of the posterior obtained through an MCMC algorithm (Gibbs sampler including PerturbationOptimization). A first numerical evaluation provides encouraging results despite the strong difficulty of the problem.

show abstract

Joint NDT Image Restoration and Segmentation Using Gauss–Markov–Potts Prior Models and Variational Bayesian Computation

Cited by 75 publications

References 36 publications

Bayesian approach with prior models which enforce sparsity in signal and image processing

Bayesian approach with prior models which enforce sparsity in signal and image processing

Images as Occlusions of Textures: A Framework for Segmentation

Deconvolution-segmentation for textured images

Contact Info

Product

Resources

About