Variance stabilizing transformations in patch-based bilateral filters for poisson noise image denoising.
Annu Int Conf IEEE Eng Med Biol Soc
; 2009: 3673-6, 2009.
Article
en En
| MEDLINE
| ID: mdl-19965000
ABSTRACT
Denoising is a key step in the processing of medical images. It aims at improving both the interpretability and visual aspect of the images. Yet, designing a robust and efficient denoising tool remains an unsolved challenge and a specific issue concerns the noise model. Many filters typically assume that noise is additive and Gaussian, with uniform variance. In contrast, noise in medical images often has more complex properties. This paper considers images with Poissonian noise and the patch-based bilateral filters, that is, filters that involve a tonal kernel and pair wise comparisons between shifted blocks of the images. The main aim is then to integrate two variance stabilizing transformations that allow the filters to work with Gaussianized noise. The performances of these filters are compared to those of the classical bilateral filter with the same transformations. The experiments include an artificial benchmark as well as a positron emission tomography image.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Procesamiento de Imagen Asistido por Computador
/
Reconocimiento de Normas Patrones Automatizadas
/
Tomografía de Emisión de Positrones
Idioma:
En
Revista:
Annu Int Conf IEEE Eng Med Biol Soc
Año:
2009
Tipo del documento:
Article
País de afiliación:
Bélgica