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A GPU Accelerated Local Polynomial Approximation Algorithm for Efficient Denoising of MR Images

  • Artur KlepaczkoEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 226)

Abstract

This paper presents a parallelized implementation of the Local Polynomial Approximation algorithm targetted at CUDA-enabled GPU hardware. Although the application area of LPA in the image processing domain is very wide, here the focus is put on magnetic resonance image de-noising. In this case, LPA serves as a pre-processing step in the method based on Shape-Adaptive Discrete Cosine Transform. It is shown, how the designed efficient implementation of LPA substantially reduces the execution time of SA-DCT.

Keywords

Shared Memory Global Memory Thread Block Streaming Multiprocessor Rician Noise 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  1. 1.Institute of ElectronicsLodz University of TechnologyLodzPoland

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