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@InProceedings{BustosKim:2005:ImMaEn,
               author = "Bustos, Harold Ivan Angulo and Kim, Hae Yong",
          affiliation = "{Universidade Federal do Rio Grande do Norte} and {Universidade de 
                         S{\~a}o Paulo}",
                title = "Reconstruction-diffusion: An improved maximum entropy 
                         reconstruction algorithm based on the robust anisotropic 
                         diffusion",
            booktitle = "Proceedings...",
                 year = "2005",
               editor = "Rodrigues, Maria Andr{\'e}ia Formico and Frery, Alejandro 
                         C{\'e}sar",
         organization = "Brazilian Symposium on Computer Graphics and Image Processing, 18. 
                         (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Maximum Entropy, Robust Anisotropic Diffusion, Tomography.",
             abstract = "Maximum entropy (MENT) is a well-known image reconstruction 
                         algorithm. If only a small amount of acquisition data is 
                         available, this algorithm converges to a noisy and blurry image. 
                         We propose an improvement to this algorithm that consists on 
                         applying alternately the MENT reconstruction and the robust 
                         anisotropic diffusion (RAD). We have tested this idea for the 
                         re-construction from full-angle parallel acquisition data, but the 
                         idea can be applied to any data acquisition sce-nario. The new 
                         technique has yielded surprisingly clear images with sharp edges 
                         even using extremely small amount of projection data.",
  conference-location = "Natal",
      conference-year = "9-12 Oct. 2005",
             language = "en",
           targetfile = "bustosh_entropy.pdf",
        urlaccessdate = "2020, Nov. 24"
}


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