Abstract
In the paper parallelization of the collaborative filtering technique for image denoising is presented. The filter is compared with several other available methods for image denoising such as Anisotropic diffusion, Wavelet packets, Total Variation denoising, Gaussian blur, Adaptive Wiener filter and Non-Local Means filter. Application of the filter is intended for denoising of the medical CT images as a part of image pre-processing before image segmentation. The paper is evaluating the filter denoising quality and describes effective parallelization of the filtering algorithm. Results of the parallelization are presented in terms of strong and weak scalability together with algorithm speed-up compared to the typical sequential version of the algorithm.
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Acknowledgements
This paper has been elaborated in the framework of the project New Creative Teams in Priorities of Scientific Research, reg. no. CZ.1.07/2.3.00/30.0055, supported by Operational Programme Education for Competitiveness and co-financed by the European Social Fund and the state budget of the Czech Republic. The work was also supported by the European Regional Development Fund in the IT4Innovations Centre of Excellence project (CZ.1.05/1.1.00/02.0070) and the Project of Major Infrastructures for Research, Development and Innovation of Ministry of Education, Youth and Sports with reg. num. LM2011033. Authors acknowledge the support of VSB-TU Ostrava under the grant SGS SP2015/189.
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Strakos, P., Jaros, M., Karasek, T., Kozubek, T. (2016). Parallel Implementation of Collaborative Filtering Technique for Denoising of CT Images. In: Kozubek, T., Blaheta, R., Šístek, J., Rozložník, M., Čermák, M. (eds) High Performance Computing in Science and Engineering. HPCSE 2015. Lecture Notes in Computer Science(), vol 9611. Springer, Cham. https://doi.org/10.1007/978-3-319-40361-8_9
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DOI: https://doi.org/10.1007/978-3-319-40361-8_9
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