Super-Resolution and Joint Segmentation in Bayesian Framework

Humblot, Fabrice

doi:10.1063/1.2149797

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…Based on this forward modeling, the inversion or the estimation of the HR image f{r) based on the LR images gi(n) and some prior modeling of the the HR image /(r) can be casted in three main classes: -Least squares (LS) methods [9,10,4,11,12] and Robust estimation (RE) methods [13,14], -Regularization based methods [15,13,14,16], and -Bayesian estimation methods [17,18,19,20,21].…”

Section: General Inversion Methodsmentioning

confidence: 99%

“…Recently, however, we developed more sophisticated methods which try to account for the fact that, very often the images to be reconstructed are composed of statistically homogeneous regions and this property can be used to develop methods who give still more accurate reconstruction results [19,20,21]. The main idea in these methods is to model the image via a composite Markov model with hidden region labels z{r) which takes discrete values k= I,--• ,K and is modeled via a Potts Markov field.…”

Section: More Advanced Prior Modelling and Bayesian Estimation Methodsmentioning

confidence: 99%

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2D and 3D Super-resolution: A Bayesian approach

Mohammad‐Djafari¹

2007

AIP Conference Proceedings

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Super-Resolution (SR) image reconstruction is the area of research and development which produces one or a set of high-resolution (HR) images from one or a set of low-resolution (LR) frames. Nowadays, there is a great number of methods of SR either for fixed or video sequence and for gray, color or hyperspectral images. There is, however, an area where we need still great effort is the 3D data, particularly in medical and biological or in non destructive testing (NDT) imaging systems where very often we may obtain low resolution 3D volumes reconstructed in different contexts (PET, Microscopy, molecular imaging, SAR imaging, holography and 3D TV, etc.) and we would like to reconstruct a HR 3D volume from a few LR ones. In this paper, first we report a few extensions to a recently developed Bayesian SR methods and then propose to extend it to 3D case.

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Section: General Inversion Methodsmentioning

confidence: 99%

Section: More Advanced Prior Modelling and Bayesian Estimation Methodsmentioning

confidence: 99%

2D and 3D Super-resolution: A Bayesian approach

Mohammad‐Djafari¹

2007

AIP Conference Proceedings

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“…• Image segmentation and images fusion [33] • Image restoration for NDT applications [34,35] • Computed Tomography (CT) for NDT applications [36,37] • Blind Sources Separation and Images separation [38,39,40,41,42,43] • Fourier Synthesis part of microwave imaging [44] • Super Resolution Images [45,46,47] • Microwave imaging for NDT [33,48,49] • Optical Diffraction Tomography [50,51] • Synthetic Aperture Radar (SAR) imaging [52] • Acoustical sources localization [53] …”

Section: Bayesian Variational Approximation With Gauss-markov-potts Pmentioning

confidence: 99%

Bayesian inference with hierarchical prior models for inverse problems in imaging systems

Mohammad-Djafari

2013

2013 8th International Workshop on Systems, Signal Processing and Their Applications (WoSSPA)

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Bayesian approach is nowadays commonly used for inverse problems. Simple prior laws (Gaussian, Generalized Gaussian, Gauss-Markov and more general Markovian priors) are common in modeling and in their use in Bayesian inference methods. But, we need still more appropriate prior models which can account for non stationnarities in signals and for the presence of the contours and homogeneous regions in images. Recently, we proposed a family of hierarchical prior models, called Gauss-MarkovPotts, which seems to be more appropriate for many applications in Imaging systems such as X ray Computed Tomography (CT) or Microwave imaging in Non Destructive Testing (NDT). In this tutorial paper, first some bacgrounds on the Bayesian inference and the tools for assignment of priors and doing efficiently the Bayesian computation is presented. Then, more specifically hiearachical models and particularly the Gauss-Markov-Potts family of prior models are presented. Finally, their real applications in image restoration, in different practical Computed Tomography (CT) or other imaging systems are presented.

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