运动模糊视频图像在图形处理器平台上的实时恢复

王晶; 李仕

doi:10.3788/OPE.20101810.2262

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运动模糊视频图像在图形处理器平台上的实时恢复

信息科学 | 更新时间：2020-08-12

- 运动模糊视频图像在图形处理器平台上的实时恢复
- Real-time restoration of motion-blurred video images on GPU
- 光学精密工程 2010年18卷第10期页码：2262-2268
- 作者机构：
  
  1. 中国科学院长春光学精密机械与物理研究所,吉林长春130033
  2. 杭州电子科技大学图形图像研究所,浙江杭州 310018
- 作者简介：
- 基金信息：
  
  国家863高技术研究发展计划资助项目(No.2008AA121803)();国家973重点基础发展规划资助项目(No.2009CB72400607)()
- DOI：10.3788/OPE.20101810.2262
  中图分类号： TN941.1;TP391.4
- 收稿日期：2010-06-07，
  
  修回日期：2010-08-06，
  
  网络出版日期：2010-10-28，
  
  纸质出版日期：2010-10-20
- 稿件说明：
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王晶, 李仕. 运动模糊视频图像在图形处理器平台上的实时恢复[J]. 光学精密工程, 2010,18(10): 2262-2268

WANG Jing, LI Shi. Real-time restoration of motion-blurred video images on GPU[J]. Editorial Office of Optics and Precision Engineering , 2010,18(10): 2262-2268
王晶, 李仕. 运动模糊视频图像在图形处理器平台上的实时恢复[J]. 光学精密工程, 2010,18(10): 2262-2268 DOI： 10.3788/OPE.20101810.2262.

WANG Jing, LI Shi. Real-time restoration of motion-blurred video images on GPU[J]. Editorial Office of Optics and Precision Engineering , 2010,18(10): 2262-2268 DOI： 10.3788/OPE.20101810.2262.

摘要

提出了一种图形处理器优化编程方法

用于实现运动模糊视频图像的实时恢复处理。根据计算统一设备架构(CUDA)的硬件框架特征对GPU的线程块及线程数量进行优化配置

并引入了一种自动内存接合访问的方法

使得GPU的硬件资源得到充分利用。根据图像频谱的对称性去除冗余信息

减少了图像算法在频谱滤波时的数据量

使得GPU对内存的访问次数下降

从而提升了算法效率。实验表明

本文提出的GPU方案的计算性能比传统的CPU平台方案提升了一个数量级

半频谱滤波设计使总时间开销减少20％以上

实验结果证明了本文方案的可行性及有效性。

Abstract

A Graphic processing Unit(GPU) optimization programming method is presented to apply to the real-time restoration of motion blurred video images. The blocks and threads run on the GPU are optimally set based on the hardware structure of Compute Unified Device Architecture (CUDA)

and a memory access method is introduced to implement automatic coalesced access. These are required to make sure the full utilization of the GPUs hardware resource. According to the symmetry property of FFT spectra

the redundant information in the frequency spectrum is eliminated and the number of frequency data filtered by the image algorithm is decreased

by which the amount of GPU memory access for realizing the algorithm optimization is reduced and the computing efficiency is improved. The experiment indicates that the proposed GPU project can improve the computing performance by 10 times as compared with the conventional CPU project

and the design of half-spectrum filtering can reduce the above time consumption by 20%. The experimental results confirm the feasibility and the validity of proposed method.

关键词

Keywords

references

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