Parallel Implementation of Collaborative Filtering Technique for Denoising of CT Images

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• 关键词：Parallelization ; Scalability ; Image filtering ; CT images ; Denoising
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9611
• 期：1
• 页码：126-140
• 全文大小：2,487 KB
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• 作者单位：Petr Strakos (18)
  Milan Jaros (18) (19)
  Tomas Karasek (18)
  Tomas Kozubek (18) (19)
  
  18. IT4Innovations, Ostrava, Czech Republic
  19. Department of Applied Mathematics, VSB - Technical University of Ostrava, Ostrava, Czech Republic
  
• 丛书名：High Performance Computing in Science and Engineering
• ISBN：978-3-319-40361-8
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9611

文摘

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.