本文選題：GPU　切入點：CUDA　出處：《蘭州交通大學》2013年碩士論文　論文類型：學位論文

【摘要】：進入二十一世紀以來，隨著科學技術的不斷發(fā)展，傳感器技術取得了長足的進步。測量獲取被測物體影像信息的方法也更加多樣化，多傳感器測量，不同角度測量，大重疊度測量都是當前遙感技術發(fā)展的趨勢之一。航空、航天影像的高分辨率、大重疊度，致使影像數(shù)據(jù)量不斷增長，海量數(shù)據(jù)處理已經(jīng)成為一種趨勢。普通的計算機受限于其存儲和計算能力，在海量數(shù)據(jù)處理面前已經(jīng)力不從心，使用CPU串行處理的生產(chǎn)方式已經(jīng)難以滿足海量數(shù)據(jù)的高效生產(chǎn)需求。 而GPU的高性能并行計算能力和可編程性則在不斷發(fā)展，為數(shù)字攝影測量中一些算法的并行化提供了很大空間，CUDA的推出更是為GPU在通用計算領域的發(fā)展提供了強有力的支撐。本文旨在研究基于GPU的并行計算技術進行影像預處理、影像增強與影像匹配，搭建GPU并行處理平臺，將GPU作為核心處理設備，基于CUDA4.0軟件開發(fā)環(huán)境，研究影像數(shù)據(jù)的GPU并行處理，以此來實現(xiàn)數(shù)據(jù)的高效處理。 本文的研究選取了PixelGrid系統(tǒng)中影像預處理算法、Wallis濾波算法、影像匹配算法作為研究對象，研究以上算法的GPU并行處理設計，著重于算法在GPU中的并行實現(xiàn)方法，并基于GPU進行了性能優(yōu)化，，以此來提高影像處理算法在攝影測量處理系統(tǒng)中的執(zhí)行效率。論文完成的主要工作和創(chuàng)新點如下: 1.簡單介紹了并行計算平臺的相關歷史和發(fā)展趨勢，對數(shù)字攝影測量影像數(shù)據(jù)并行處理的基本模式進行了歸納。系統(tǒng)的論述了GPU的硬件體系架構，CUDA軟件編程模型，以及CUDA程序的優(yōu)化，給出了論文研究所采用的實驗平臺。 2.提出了一種影像預處理GPU并行算法，將影像的旋轉、畸變差改正通過GPU進行并行處理，在重采樣操作中，利用GPU進行細粒度并行處理，并根據(jù)算法的特點和GPU的體系架構，優(yōu)化了任務劃分與執(zhí)行配置方案，充分發(fā)揮GPU的并行計算優(yōu)勢。 3.提出GPU加速的Wallis影像增強并行算法，算法中加入基于GPU的自適應平滑濾波，利用GPU強大的并行能力，實現(xiàn)了Wallis濾波以及影像自適應平滑的GPU并行處理，在算法中運用了共享存儲器對速度進行優(yōu)化。 4.提出了一種基于GPU的Harris算子影像匹配并行處理方法，在GPU中完成對影像的灰度化、Harris角點提取，重采樣、灰度相關匹配，并從線程分配、內(nèi)存使用、共享存儲器(share memory)等方面進行優(yōu)化，充分的發(fā)揮出GPU的巨大并行計算能力。實驗結果表明，該方法與CPU串行處理方法相比，其速度得到了明顯提升。
[Abstract]:Since 21th century, with the development of science and technology, sensor technology has made great progress. The measurement of large overlap degree is one of the trends in the development of remote sensing technology at present. The high resolution and large overlap degree of aerial and spaceflight images make the amount of image data increase continuously. Mass data processing has become a trend. Ordinary computers, limited by their storage and computing capabilities, have been unable to cope with mass data processing. The production mode of serial processing with CPU is difficult to meet the high efficient production demand of massive data. The high performance parallel computing power and programmability of GPU are developing continuously. It provides a large space for the parallelization of some algorithms in digital photogrammetry and provides a strong support for the development of GPU in the field of general computing. This paper aims to study the parallel computing technology based on GPU for image preprocessing. Image enhancement and image matching, build GPU parallel processing platform, take GPU as the core processing equipment, based on the CUDA4.0 software development environment, study the GPU parallel processing of the image data, so as to realize the efficient processing of the data. In this paper, we select the image preprocessing algorithm and image matching algorithm in PixelGrid system as the research object, study the GPU parallel processing design of the above algorithms, and focus on the parallel implementation of the algorithm in GPU. The performance optimization based on GPU is carried out to improve the efficiency of image processing algorithm in photogrammetric processing system. The main work and innovation of this paper are as follows:. 1. The history and development trend of parallel computing platform are briefly introduced, and the basic mode of parallel processing of digital photogrammetric image data is summarized. The hardware architecture of GPU and the software programming model of CUDA are discussed systematically. As well as the optimization of CUDA program, the experimental platform used in this paper is given. 2. An image preprocessing GPU parallel algorithm is proposed, in which image rotation and aberration correction are processed in parallel by GPU. In resampling operation, fine-grained parallel processing is performed by using GPU. According to the characteristics of the algorithm and the architecture of GPU, a parallel algorithm is proposed. The task partition and execution configuration scheme is optimized to give full play to the advantages of parallel computing of GPU. 3. The Wallis image enhancement parallel algorithm accelerated by GPU is proposed. The adaptive smoothing filter based on GPU is added to the algorithm, and the Wallis filter and the GPU parallel processing of image adaptive smoothing are realized by using the powerful parallel ability of GPU. The shared memory is used to optimize the speed of the algorithm. 4. A parallel processing method of Harris operator image matching based on GPU is proposed. The grayscale image is extracted from GPU, resampling, grayscale correlation matching, thread allocation and memory usage. Share memory is optimized to give full play to the huge parallel computing power of GPU. The experimental results show that the speed of this method is obviously improved compared with the CPU serial processing method.
【學位授予單位】：蘭州交通大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：P231.5

【參考文獻】

相關期刊論文 前10條

1 李卓;邱慧娟;;基于相關系數(shù)的快速圖像匹配研究[J];北京理工大學學報;2007年11期

2 肖漢;張祖勛;;基于GPGPU的并行影像匹配算法[J];測繪學報;2010年01期

3 李建江;張磊;李興鋼;陳翔;黃義雙;;CUDA架構下的灰度圖像匹配并行算法[J];電子科技大學學報;2012年01期

4 張祖勛;柯濤;郭大海;王建超;;數(shù)字攝影測量網(wǎng)格在汶川大地震中的快速響應[J];中國工程科學;2009年06期

5 宋驥;周松濤;;基于GPU并行計算的圖像快速匹配[J];湖北民族學院學報(自然科學版);2011年03期

6 盧文龍;王建軍;劉曉軍;;基于CUDA的高速并行高斯濾波算法[J];華中科技大學學報(自然科學版);2011年05期

7 盧俊;張保明;黃薇;李二森;;基于GPU的遙感影像數(shù)據(jù)融合IHS變換算法[J];計算機工程;2009年07期

8 肖江;胡柯良;鄧元勇;;基于CUDA的矩陣乘法和FFT性能測試[J];計算機工程;2009年10期

9 吳恩華,柳有權;基于圖形處理器(GPU)的通用計算[J];計算機輔助設計與圖形學學報;2004年05期

10 張力,張祖勛,張劍清;Wallis濾波在影像匹配中的應用[J];武漢測繪科技大學學報;1999年01期

本文編號：1572161