中南大学学报（自然科学版）2017，Vol.48Issue(10)：2691-2696,6.DOI:10.11817/j.issn.1672-7207.2017.10.019
基于GPU的小尺寸FFT在实时图像复原中的优化
Optimization on FFT of small size in real-time image restoration based on GPU
摘要
Abstract
To meet the real-time demand of image restoration for recognition and tracking system, an optimization research on two-dimensional FFT of small size realized in graphics processor unit(GPU) efficiently was done. An analysis of two-dimensional FFT algorithm was analyzed first. And according to the characteristics of GPU, a parallel execution model based on graphics processor was proposed. Based on this model, the optimization research was done considering the aspects of algorithm complexity, the number of jump instructions, access conflict and access latency of the shared memory, and the utilization efficiency of GPU. And two-dimensional FFT computation of small size was realized in the GPU. In image restoration experiment, comparison on the calculation accuracy of two-dimensional FFT of small size optimization algorithm based on GPU and the traditional algorithm in MATLAB based on CPU was done. And a comparison on the computational efficiency of optimization algorithm proposed and the library function image restoration algorithm of CUFFT provided by NVIDIA Corp in four different sizes based on the same GPU platform was made. The results indicate that this optimization algorithm has excellent recovery performance, and human vision system could not distinguish the difference between the results and the MATLAB demonstrations. And the optimization algorithm can recover a frame of 128×128 gray fuzzy image within 19.6 ms, while the computing speed increases 1.8 times approximately compared with that using library function of CUFFT directly.关键词
图形处理器/小尺寸FFT/图像复原/并行优化/实时处理Key words
graphic processing unit (GPU)/FFT of small size/image restoration/parallel optimization/real-time computation分类
信息技术与安全科学引用本文复制引用
严发宝,苏艳蕊,赵占锋,左颢睿,柳建新..基于GPU的小尺寸FFT在实时图像复原中的优化[J].中南大学学报（自然科学版）,2017,48(10):2691-2696,6.基金项目
国家科技基础性工作专项(2013FY110800) （2013FY110800）
中国博士后科学基金资助项目(2016M600538) （2016M600538）
国家自然科学基金资助项目(41674080,41574123,21505028)(Project(2013FY110800) supported by the National Science and Technology Basic Work （41674080,41574123,21505028）
Project (2016M600538) funded by China Postdoctoral Science Foundation （2016M600538）
Projects(41674080, 41574123, 21505028) supported by the National Science Foundation of China) （41674080, 41574123, 21505028）
