Abstract
We introduced a real time Image Processing technique using modern programmable Graphic Processing Units (GPU) in this paper. GPU is a SIMD (Single Instruction, Multiple Data) device that is inherently data-parallel. By utilizing NVIDIA’s new GPU Programming framework, “Compute Unified Device Architecture” (CUDA) as a computational resource, we realize significant acceleration in the computations of different Image processing Algorithms. Here we present an efficient implementation of algorithms on the NVIDIA GPU. Specifically, we demonstrate the efficiency of our approach by a parallelization and optimization of the algorithm. In result we show time comparison between CPU and GPU implementation.
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Daga, B., Bhute, A., Ghatol, A. (2011). Implementation of Parallel Image Processing Using NVIDIA GPU Framework. In: Unnikrishnan, S., Surve, S., Bhoir, D. (eds) Advances in Computing, Communication and Control. ICAC3 2011. Communications in Computer and Information Science, vol 125. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18440-6_58
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DOI: https://doi.org/10.1007/978-3-642-18440-6_58
Publisher Name: Springer, Berlin, Heidelberg
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