Abstract
This paper demonstrates a massively multi-threaded implementation of super-resolution image formation on the NVIDIA CUDA architecture. On the algorithm side maximum a-posteriori (MAP) reconstruction is adopted with sub-pixel translational motion estimation algorithm for spatial resolution enhancement. Resulting algorithm is implemented in CUDA using a low end GT 640 GPU, and an overall speed up of 10 – 11 times is achieved compared to ANSI C implementation running on a Core i5 CPU.
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References
NVIDIA CUDA C Programming Guide, http://developer.download.nvidia.com/compute/DevZone/docs/html/C/doc/CUDA_C_Programming_Guide.pdf
Gevrekci, M., Gunturk, B.K.: Image Acquisition Modeling for Super-Resolution Reconstruction. In: IEEE Int. Conf. on Image Processing (ICIP), vol. 2, pp. 1058–1061 (September 2005)
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© 2013 Springer International Publishing Switzerland
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Akgün, T., Gevrekci, M. (2013). Accelerating Super-Resolution Reconstruction Using GPU by CUDA. In: Nagamalai, D., Kumar, A., Annamalai, A. (eds) Advances in Computational Science, Engineering and Information Technology. Advances in Intelligent Systems and Computing, vol 225. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00951-3_5
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DOI: https://doi.org/10.1007/978-3-319-00951-3_5
Publisher Name: Springer, Heidelberg
Print ISBN: 978-3-319-00950-6
Online ISBN: 978-3-319-00951-3
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