@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, RN, Brazil",
conference-year = "9-12 Oct. 2005",
doi = "10.1109/SIBGRAPI.2005.42",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2005.42",
language = "en",
ibi = "6qtX3pFwXQZeBBx/GKpLx",
url = "http://urlib.net/ibi/6qtX3pFwXQZeBBx/GKpLx",
targetfile = "bustosh_entropy.pdf",
urlaccessdate = "2024, Apr. 27"
}