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Efficient parallelization on GPU of an image smoothing method based on a variational model

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Abstract

Medical imaging is fundamental for improvements in diagnostic accuracy. However, noise frequently corrupts the images acquired, and this can lead to erroneous diagnoses. Fortunately, image preprocessing algorithms can enhance corrupted images, particularly in noise smoothing and removal. In the medical field, time is always a very critical factor, and so there is a need for implementations which are fast and, if possible, in real time. This study presents and discusses an implementation of a highly efficient algorithm for image noise smoothing based on general purpose computing on graphics processing units techniques. The use of these techniques facilitates the quick and efficient smoothing of images corrupted by noise, even when performed on large-dimensional data sets. This is particularly relevant since GPU cards are becoming more affordable, powerful and common in medical environments.

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Notes

  1. CUDA compiler and development suite are available to download through the NVIDIA Web site https://developer.nvidia.com/cuda-downloads.

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Acknowledgments

The first author would like to thank the “Universidade do Estado de Mato Grosso” (UNEMAT), in Brazil, for the support given. The National Scientific and Technological Development Council (CNPq) partially supported this work through process 234360/2014-9 and Grant 2010/15691-0. Henrique Ferraz de Arruda thanks the Coordination for the Improvement of Higher Education Personnel (CAPES) for the financial support received. Authors gratefully acknowledge the funding of Project NORTE-01-0145-FEDER-000022 - SciTech - Science and Technology for Competitive and Sustainable Industries, cofinanced by “Programa Operacional Regional do Norte” (NORTE2020), through “Fundo Europeu de Desenvolvimento Regional” (FEDER).

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Authors and Affiliations

  1. Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal

    Carlos A. S. J. Gulo & Alex F. de Araujo

  2. Instituto de Ciências Matemática e de Computação, Universidade de São Paulo, São Paulo, Brasil

    Henrique F. de Arruda

  3. Departamento de Ciências da Computação, Universidade Estadual Paulista-UNESP, Bauru, Brasil

    Antonio C. Sementille

  4. Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

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  1. Carlos A. S. J. Gulo
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Correspondence to João Manuel R. S. Tavares.

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Gulo, C.A.S.J., de Arruda, H.F., de Araujo, A.F. et al. Efficient parallelization on GPU of an image smoothing method based on a variational model. J Real-Time Image Proc 16, 1249–1261 (2019). https://doi.org/10.1007/s11554-016-0623-x

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