Adaptive regularized super-resolution reconstruction for magnetic resonance images / 南方医科大学学报
Journal of Southern Medical University
;
(12): 1705-1708, 2011.
Artículo
en Chino
| WPRIM
| ID: wpr-333832
ABSTRACT
To increase the resolution and signal-to-noise ratio (SNR) of magnetic resonance (MR) images, an adaptively regularized super-resolution reconstruction algorithm was proposed and applied to acquire high resolution MR images from 4 subpixel-shifted low resolution images on the same anatomical slice. The new regularization parameter, which allowed the cost function of the new algorithm to be locally convex within the definition region, was introduced by the piori information to enhance detail restoration of the image with a high frequency. The experiment results proved that the proposed algorithm was superior to other counterparts in achieving the reconstruction of low-resolution MR images.
Texto completo:
Disponible
Índice:
WPRIM (Pacífico Occidental)
Asunto principal:
Algoritmos
/
Procesamiento de Imagen Asistido por Computador
/
Imagen por Resonancia Magnética
/
Aumento de la Imagen
/
Métodos
Límite:
Humanos
Idioma:
Chino
Revista:
Journal of Southern Medical University
Año:
2011
Tipo del documento:
Artículo
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