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		<site>sibgrapi.sid.inpe.br 802</site>
		<citationkey>BustosKim:2005:ImMaEn</citationkey>
		<author>Bustos, Harold Ivan Angulo,</author>
		<author>Kim, Hae Yong,</author>
		<affiliation>Universidade Federal do Rio Grande do Norte</affiliation>
		<affiliation>Universidade de São Paulo</affiliation>
		<title>Reconstruction-diffusion: An improved maximum entropy reconstruction algorithm based on the robust anisotropic diffusion</title>
		<conferencename>Brazilian Symposium on Computer Graphics and Image Processing, 18 (SIBGRAPI)</conferencename>
		<year>2005</year>
		<editor>Rodrigues, Maria Andréia Formico,</editor>
		<editor>Frery, Alejandro César,</editor>
		<booktitle>Proceedings</booktitle>
		<date>9-12 Oct. 2005</date>
		<publisheraddress>Los Alamitos</publisheraddress>
		<publisher>IEEE Computer Society</publisher>
		<conferencelocation>Natal</conferencelocation>
		<keywords>Maximum Entropy, Robust Anisotropic Diffusion, Tomography.</keywords>
		<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.</abstract>
		<language>en</language>
		<tertiarytype>Full Paper</tertiarytype>
		<format>On-line</format>
		<size>194 KiB</size>
		<numberoffiles>1</numberoffiles>
		<targetfile>bustosh_entropy.pdf</targetfile>
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		<e-mailaddress>harold@dca.ufrn.br;  hae@lps.usp.br</e-mailaddress>
		<usergroup>Harold administrator</usergroup>
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		<url>http://sibgrapi.sid.inpe.br/rep-/sid.inpe.br/banon/2005/07.13.18.55</url>
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