`%0 Conference Proceedings`

`%4 sid.inpe.br/banon/2005/07.13.18.55`

`%2 sid.inpe.br/banon/2005/07.13.18.55.30`

`%T Reconstruction-diffusion: An improved maximum entropy reconstruction algorithm based on the robust anisotropic diffusion`

`%D 2005`

`%A Bustos, Harold Ivan Angulo,`

`%A Kim, Hae Yong,`

`%@affiliation Universidade Federal do Rio Grande do Norte`

`%@affiliation Universidade de São Paulo`

`%E Rodrigues, Maria Andréia Formico,`

`%E Frery, Alejandro César,`

`%B Brazilian Symposium on Computer Graphics and Image Processing, 18 (SIBGRAPI)`

`%C Natal`

`%8 9-12 Oct. 2005`

`%I IEEE Computer Society`

`%J Los Alamitos`

`%S Proceedings`

`%K Maximum Entropy, Robust Anisotropic Diffusion, Tomography.`

`%X 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.`

`%@language en`

`%3 bustosh_entropy.pdf`