Real-time HD image distortion correction in heterogeneous parallel computing systems using efficient memory access patterns

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• 作者：Rui Melo ; Gabriel Falcao ; João P. Barreto
• 关键词：Radial distortion ; Heterogeneous architecture ; Medical endoscopy ; Concurrent processing ; GPU optimization
• 刊名：Journal of Real-Time Image Processing
• 出版年：2016
• 出版时间：January 2016
• 年：2016
• 卷：11
• 期：1
• 页码：83-91
• 全文大小：1,405 KB
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• 作者单位：Rui Melo (1)
  Gabriel Falcao (2)
  João P. Barreto (1)
  
  1. Department of Electrical and Computer Engineering, Faculty of Science and Technology, Institute for Systems and Robotics, University of Coimbra, 3000-214, Coimbra, Portugal
  2. Department of Electrical and Computer Engineering, Faculty of Science and Technology, Instituto de Telecomunicações, University of Coimbra, 3030-290, Coimbra, Portugal
  
• 刊物类别：Computer Science
• 刊物主题：Image Processing and Computer Vision
  Multimedia Information Systems
  Computer Graphics
  Pattern Recognition
  Signal,Image and Speech Processing
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1861-8219

文摘

High-definition video is becoming a standard in clinical endoscopy. State-of-the-art systems for medical endoscopy provide 1080p video streams at 60 Hz. For such high resolutions and frame rates, the real-time execution of image-processing tasks is far from trivial, requiring careful algorithm design and development. In this article, we propose a fully functional software-based solution for correcting the radial distortion (RD) of HD video that runs in real time in a personal computer (PC) equipped with a conventional graphics processing unit (GPU) and a video acquisition card. Our system acquires the video feed directly from the digital output of the endoscopic camera control unit, warps each frame using a heterogeneous parallel computing architecture, and outputs the result back to the display. Although we target the particular problem of correcting geometric distortion in medical endoscopy, the concepts and framework herein described can be extended to other image-processing tasks with hard real-time requirements. We show that a heterogeneous approach, as well as efficient memory access patterns in the GPU, improve the performance of this highly memory-bound algorithm, leading to frame rates above 250 fps.