Acceleration of 3D feature-enhancing noise filtering in hybrid CPU/GPU systems
FlowDenoising is a new approach to noise reduction in biological volumes obtained with three-dimensional electron microscopy (3DEM). Its abilities to enhance the structural features stem from the fact that an anisotropic Gaussian filtering is steered according to the local structures. To this end, t...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/360190 |
| Acceso en línea: | http://hdl.handle.net/10261/360190 |
| Access Level: | acceso abierto |
| Palabra clave: | High-performance computing GPU CPU Heterogeneous computing Noise fltering 3D electron microscopy Optical fow |
| Sumario: | FlowDenoising is a new approach to noise reduction in biological volumes obtained with three-dimensional electron microscopy (3DEM). Its abilities to enhance the structural features stem from the fact that an anisotropic Gaussian filtering is steered according to the local structures. To this end, the Optical Flow (OF) among consecutive slices is estimated, which is the most computationally expensive step in this approach. In this article, a hybrid CPU/GPU implementation of FlowDenoising is introduced and evaluated. It exploits parallel computing by distributing the workload among multiple cores and takes advantage of the massive processing in GPUs to accelerate the OF estimation. The hybrid implementation provides remarkable speed-up factors and an important reduction of the processing time, which is particularly relevant for the denoising of huge volumes typically found in 3DEM. |
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