Data/hora: 23/06/2024 19:53:11

Biblioteca(s): | Embrapa Agricultura Digital.
Data corrente: | 13/03/1998
Data da última atualização: | 03/08/2007
Autoria: | MASCARENHAS, N. D. A.; SANTOS, S. S. L.; CRUVINEL, P. E.
Título: | The use of MAP estimation techniques in the tomographic reconstruction of poisson noise corrupted images.
Ano de publicação: | 1996
Fonte/Imprenta: | In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO GRÁFICA E PROCESSAMENTO DE IMAGENS, 9., 1996, Caxambu. Anais... Caxambu: SBC, 1996.
Páginas: | p.197-204.
Idioma: | Inglês
Notas: | Evento conhecido como: SIBGRAPI. Editado por L. Velho, A. de Albuquerque e R. A. Lotufo.
Conteúdo: | The problem of tomographic image reconstruction is important in many areas of applied science and technology. This work presents new methods for the tomographic recreconstruction of images with poisson noise corrupted projections. The poisson noise comes from the discrete nature of radiation that characterizes the counting process of particles in the projection set. The reconstruction method is performed by first filtering the noisy projections under the maximum a posterion criterion and subsequently reconstructing the images through conventional filtering - backprojection methods using the ramp filler. The "a priori" knowledge  is incorporated by using several densities, including the gaussian and densities defined on the non-negative real line. These densities were used to denote the fact the rates of counting on the projections are non-negative quantities. Experimental results include the filtering of simulated noisy projections and the reconstruction of simulated and real phantoms scanned with a minitomograph scanner for soil science. The obtained results indicate that, by using the MAP criterion, it is possible to obtain a lower mean square error in reconstruction, as compared to conventional methods of filtering-backrojection, with a very small increase in computational effort.
Palavras-Chave: | Computacao grafica; Graphic computation; Image processing; Processamento de imagens.
Categoria do assunto: | --
Registro original: | Embrapa Agricultura Digital (CNPTIA)
Exemplares: | CNPTIA - (6710-1) - ADD - PL - -- - 006.6 - SIM - 1996.00014 - DIS