Image Reconstruction based on Bayesian total variation and Hidden Markov model
| Autor: | Yasser Haddadi, Zouaoui Chama, Boualem Mansouri, Ali Mohammad Djafari |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Tomographic reconstruction
Contextual image classification Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020206 networking & telecommunications 02 engineering and technology Iterative reconstruction Inverse problem Real image Regularization (mathematics) 03 medical and health sciences symbols.namesake 0302 clinical medicine Fourier transform 0202 electrical engineering electronic engineering information engineering symbols Hidden Markov model Algorithm 030217 neurology & neurosurgery |
| Zdroj: | 2019 6th International Conference on Image and Signal Processing and their Applications (ISPA). |
| DOI: | 10.1109/ispa48434.2019.8966879 |
| Popis: | Several methods of inversion in Fourier synthesis are based on interpolation of the data and fast inverse Fourier Transform (FT), but, the results obtained by such methods are not satisfactory when the data do not fill uniformly the Fourier domain which is the case in many applications in tomographic imaging because this inverse problem is known to be nonlinear and ill-posed. In this paper, we propose a regularization method based on the Bayesian estimation by introducing some prior information. In the first step, we assume that knowledge of the noise nature is considered as a prior information, for this and in order to improve the quality of image reconstruction, we use a total variation prior whose model parameter is automatically determined using the Evidence Analysis within the Hierarchical Bayesian Paradigm. Secondly, we assume that the original image is composed of homogeneous regions, in this case we propose the Hidden Markov Modelling for image classification. This approach allows to have an algorithm that unifies restoration and reconstruction via classification in Fourier synthesis. This method is applied on synthetics and real images. |
| Databáze: | OpenAIRE |
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