Abstract
Super Resolution (SR) refers to the reconstruction of a high resolution image from one or more low resolution images for the same scene. The reconstruction process is considered an inverse problem to the observation model. In this paper the SR problem is formulated by using Support Vector Regression (SVR). SVR is a very expensive computationally algorithm, thus it could be accelerated by using the computational power of a Graphics Processing Unit (GPU). The proposed parallel SVR has been implemented using NVidia’s compute device unified architecture (CUDA). An experiment has been done for a real satellite image. The experimental result demonstrates the speedup of the presented GPU implementation and compared with the serial CPU implementation and state-of-the-art techniques. The speedup of the presented SVR GPU-based implementation is up to approximately 50 times faster than the corresponding optimized CPU.
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Moustafa, M., Ebied, H.M., Helmy, A., Nazamy, T.M., Tolba, M.F. (2014). Satellite Super Resolution Image Reconstruction Based on Parallel Support Vector Regression. In: Hassanien, A.E., Tolba, M.F., Taher Azar, A. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2014. Communications in Computer and Information Science, vol 488. Springer, Cham. https://doi.org/10.1007/978-3-319-13461-1_22
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DOI: https://doi.org/10.1007/978-3-319-13461-1_22
Publisher Name: Springer, Cham
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