@InProceedings{8081469,
  author = {C. Nikou},
  booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
  title = {MAP tomographic reconstruction with a spatially adaptive hierarchical image model},
  year = {2017},
  pages = {1549-1553},
  abstract = {A method for penalized likelihood tomographic reconstruction is presented which is based on a spatially adaptive stochastic image model. The model imposes onto the image a smoothing Gaussian prior whose parameters follow a Gamma distribution. Three variations of the model are examined: (i) a stationary model, where the Gamma distribution has the same constant parameter for the entire image, (ii) a non stationary model, where this parameter varies with respect to location and (iii) a non stationary directional model where the parameter varies also with respect to orientation (horizontal or vertical direction). In all cases, the MAP criterion provides a closed form solution for both the unknown image and the parameters of the model. Numerical experiments showed that the reconstructions obtained using the proposed image priors outperform the state of the art EM based methods.},
  keywords = {gamma distribution;image reconstruction;iterative methods;maximum likelihood estimation;stochastic processes;constant parameter;MAP criterion;image priors;MAP tomographic reconstruction;spatially adaptive hierarchical image model;penalized likelihood tomographic reconstruction;spatially adaptive stochastic image model;Gamma distribution;stationary model;image reconstructions;EM based methods;smoothing Gaussian prior;horizontal direction;vertical direction;closed form solution;Adaptation models;Image reconstruction;Computational modeling;Tomography;Mathematical model;Europe;Signal processing},
  doi = {10.23919/EUSIPCO.2017.8081469},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570348171.pdf},
}

{"_id":"QZJSFKoXrtY6k36Sm","bibbaseid":"nikou-maptomographicreconstructionwithaspatiallyadaptivehierarchicalimagemodel-2017","authorIDs":[],"author_short":["Nikou, C."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["C."],"propositions":[],"lastnames":["Nikou"],"suffixes":[]}],"booktitle":"2017 25th European Signal Processing Conference (EUSIPCO)","title":"MAP tomographic reconstruction with a spatially adaptive hierarchical image model","year":"2017","pages":"1549-1553","abstract":"A method for penalized likelihood tomographic reconstruction is presented which is based on a spatially adaptive stochastic image model. The model imposes onto the image a smoothing Gaussian prior whose parameters follow a Gamma distribution. Three variations of the model are examined: (i) a stationary model, where the Gamma distribution has the same constant parameter for the entire image, (ii) a non stationary model, where this parameter varies with respect to location and (iii) a non stationary directional model where the parameter varies also with respect to orientation (horizontal or vertical direction). In all cases, the MAP criterion provides a closed form solution for both the unknown image and the parameters of the model. Numerical experiments showed that the reconstructions obtained using the proposed image priors outperform the state of the art EM based methods.","keywords":"gamma distribution;image reconstruction;iterative methods;maximum likelihood estimation;stochastic processes;constant parameter;MAP criterion;image priors;MAP tomographic reconstruction;spatially adaptive hierarchical image model;penalized likelihood tomographic reconstruction;spatially adaptive stochastic image model;Gamma distribution;stationary model;image reconstructions;EM based methods;smoothing Gaussian prior;horizontal direction;vertical direction;closed form solution;Adaptation models;Image reconstruction;Computational modeling;Tomography;Mathematical model;Europe;Signal processing","doi":"10.23919/EUSIPCO.2017.8081469","issn":"2076-1465","month":"Aug","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570348171.pdf","bibtex":"@InProceedings{8081469,\n author = {C. Nikou},\n booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},\n title = {MAP tomographic reconstruction with a spatially adaptive hierarchical image model},\n year = {2017},\n pages = {1549-1553},\n abstract = {A method for penalized likelihood tomographic reconstruction is presented which is based on a spatially adaptive stochastic image model. The model imposes onto the image a smoothing Gaussian prior whose parameters follow a Gamma distribution. Three variations of the model are examined: (i) a stationary model, where the Gamma distribution has the same constant parameter for the entire image, (ii) a non stationary model, where this parameter varies with respect to location and (iii) a non stationary directional model where the parameter varies also with respect to orientation (horizontal or vertical direction). In all cases, the MAP criterion provides a closed form solution for both the unknown image and the parameters of the model. Numerical experiments showed that the reconstructions obtained using the proposed image priors outperform the state of the art EM based methods.},\n keywords = {gamma distribution;image reconstruction;iterative methods;maximum likelihood estimation;stochastic processes;constant parameter;MAP criterion;image priors;MAP tomographic reconstruction;spatially adaptive hierarchical image model;penalized likelihood tomographic reconstruction;spatially adaptive stochastic image model;Gamma distribution;stationary model;image reconstructions;EM based methods;smoothing Gaussian prior;horizontal direction;vertical direction;closed form solution;Adaptation models;Image reconstruction;Computational modeling;Tomography;Mathematical model;Europe;Signal processing},\n doi = {10.23919/EUSIPCO.2017.8081469},\n issn = {2076-1465},\n month = {Aug},\n url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570348171.pdf},\n}\n\n","author_short":["Nikou, C."],"key":"8081469","id":"8081469","bibbaseid":"nikou-maptomographicreconstructionwithaspatiallyadaptivehierarchicalimagemodel-2017","role":"author","urls":{"Paper":"https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570348171.pdf"},"keyword":["gamma distribution;image reconstruction;iterative methods;maximum likelihood estimation;stochastic processes;constant parameter;MAP criterion;image priors;MAP tomographic reconstruction;spatially adaptive hierarchical image model;penalized likelihood tomographic reconstruction;spatially adaptive stochastic image model;Gamma distribution;stationary model;image reconstructions;EM based methods;smoothing Gaussian prior;horizontal direction;vertical direction;closed form solution;Adaptation models;Image reconstruction;Computational modeling;Tomography;Mathematical model;Europe;Signal processing"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/Roznn/EUSIPCO/main/eusipco2017url.bib","creationDate":"2021-02-13T16:38:25.684Z","downloads":0,"keywords":["gamma distribution;image reconstruction;iterative methods;maximum likelihood estimation;stochastic processes;constant parameter;map criterion;image priors;map tomographic reconstruction;spatially adaptive hierarchical image model;penalized likelihood tomographic reconstruction;spatially adaptive stochastic image model;gamma distribution;stationary model;image reconstructions;em based methods;smoothing gaussian prior;horizontal direction;vertical direction;closed form solution;adaptation models;image reconstruction;computational modeling;tomography;mathematical model;europe;signal processing"],"search_terms":["map","tomographic","reconstruction","spatially","adaptive","hierarchical","image","model","nikou"],"title":"MAP tomographic reconstruction with a spatially adaptive hierarchical image model","year":2017,"dataSources":["2MNbFYjMYTD6z7ExY","uP2aT6Qs8sfZJ6s8b"]}