Iterative methods have become a hot topic of research in computed tomography (CT) imaging because of their capacity to resolve the reconstruction problem from a limited number of projections. This allows the reduction of radiation exposure on patients during the data acquisition. The reconstruction time and the high radiation dose imposed on patients are the two major drawbacks in CT. To solve them effectively we adapted the method for sparse linear equations and sparse least squares (LSQR) with soft threshold filtering (STF) and the fast iterative shrinkage-thresholding algorithm (FISTA) to computed tomography reconstruction. The feasibility of the proposed methods are demonstrated numerically.

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ResumenSin lugar a dudas, una de las tareas más complejas y que más tiempo consume enInternet está revolucionando al mundo pero eso no es un secreto. El rápido progreso de las Tecnologías de la Información y Comunicación (TIC's) continúa modificando la forma de elaborar, adquirir y transmitir los conocimientos, es por eso que los sistemas educativos con sus modelos y estrategias se han visto en la necesidad de adaptarse a una sociedad que está cada vez más sumergida en las TIC's, ya que éstas han brindado posibilidades de renovar el contenido de los cursos y métodos pedagógicos.La educación se vuelve cada vez más competitiva y para alcanzar un mejor nivel educativo se requiere del apoyo de recursos que nos ayuden en el proceso de enseñanza de los estudiantes, como lo son los materiales didácticos, su uso tiende a guiar y motivar al estudiante en la construcción del conocimiento, es decir, que sirvan de apoyo en el proceso de aprendizaje de los estudiantes mediante publicaciones de sistemas pedagógicos innovadores utilizando herramientas tecnológicas.

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In this paper, we report a study on the parallelization of an algorithm for removing impulsive noise in images. The algorithm is based on the concept of peer group and fuzzy metric. We have developed implementations using Open Multi-Processing (OpenMP) and Compute Unified Device Architecture (CUDA) for Graphics Processing Unit (GPU). Many sequential algorithms have been proposed to remove noise, but their computational cost is excessive for real-time processing of large images. We developed implementations for a multi-core CPU, for a multi-GPU (several GPUs) and for a combination of both. These implementations were compared also with different sizes of the image in order to find out the settings with the best performance. A study is made using the shared memory and texture memory to minimize access time to data in GPU global memory. The result shows that when the image is distributed in multicore and multi-GPU a greater number of Mpixels/second are processed.