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

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。

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 GPU＇s 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.