基于GPU加速的实时视频超分辨率重建  

Real-time video super-resolution restruction based on GPU acceleration

作　　者：陈湘骥[1,2] 韩国强[2] 张芝源 

机构地区：[1]华南农业大学信息学院,广州510642 [2]华南理工大学计算机科学与工程学院,广州510006

出　　处：《计算机应用》2013年第12期3540-3543,共4页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61070090;61003270);国家自然科学广东联合基金资助项目(U1035004);广东省工业科技计划项目(2009B030803004;2008B080701052)

摘　　要：基于稀疏表示的超分辨率算法的图像重建质量好,但算法复杂,现有的CPU串行执行算法无法满足视频实时处理的需要。为此提出了基于GPU加速的稀疏表示的实时视频超分辨率算法。该算法着重于优化数据并行处理流程,提高GPU资源利用率,通过设置视频帧队列、提高显存访问并发率、采用主成分分析(PCA)降维、优化字典查找等手段,使算法执行速度比现有CPU串行算法提高了2个数量级,在显示分辨率为669×546的视频回放测试中达到每秒33帧。The methods of image super-resolution via sparse representation achieve good quality image reconstruction, but the CPU-based implementation of the methods hardly satisfies the requirement of real-time video super-resolution because of high computational complexity. Then, the method of real-time video super-resolution via sparse representation based on GPU acceleration was proposed. It focused on optimizing data parallel processing and improving resource utilization of GPU, including utilizing queues for video sequences, improving memory concurrent access rates, employing Principal Component Analysis （PCA） dimensionality reduction and optimizing dictionary querying operation. As a result, compared with the CPU- based implementation, the speed of data processing is increased two ofders of magnitude, and the speed of playing a video ~sith the size of 669- 546 reaches 33 frames per second.

关 键 词：视频 实时 超分辨率 稀疏表示 通用计算图形处理器 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]