基于CUDA的影像配準與拼接方法研究
發(fā)布時間：2018-07-30 08:33
【摘要】：圖像作為客觀世界能量或狀態(tài)的可視化形式,為人們認知和改造客觀世界提供了豐富的信息。而圖像配準和圖像拼接作為圖像處理的基本問題之一,在虛擬現(xiàn)實、地質勘測、醫(yī)學影像、氣象預測、應急響應等領域都有著廣泛的應用。 圖形處理器GPU將更多的晶體管用作執(zhí)行單元,計算能力遠遠超過傳統(tǒng)的中央處理器CPU。目前,GPU技術已經(jīng)廣泛用于數(shù)據(jù)挖掘、數(shù)理統(tǒng)計、圖像語音識別、基因工程、全球氣候準確預報等領域,同時也為遙感影像的快速處理提供了一種新的解決方案。 針對當前影像配準和拼接計算中存在問題,本文在分析當前影像配準和拼接技術的基礎上,結合CUDA并行計算技術,重點研究了大尺寸高分辨率遙感影像配準方法以及航空影像的在線實時拼接方法。本文的主要研究內容包括：(1)大尺寸遙感影像配準。針對傳統(tǒng)遙感影像配準方法難以適應于大尺寸高分辨率遙感影像配準的問題,本文提出一種由粗到精的配準控制點匹配方法；在此基礎上,采用小面元微分糾正方法實現(xiàn)大尺寸影像的高精度糾正,并采用一種自適應的掃描線填充算法來計算每個糾正像素所在的三角形；本文通過分析配準中各個步驟的計算瓶頸問題,利用CUDA并行計算技術對控制點匹配和影像糾正兩個階段進行了加速。通過IKONOS全色影像、Geoeye全色影像和多光譜影像、ZY-3衛(wèi)星影像等實驗表明,本文方法可以取得較高的配準精度,且利用GPU加速算法獲得了較高的加速比。(2)航空影像實時在線拼接。針對航空影像在線實時拼接的計算瓶頸,本文提出了一種基于CPU與GPU協(xié)同處理的在線拼接方法�；赑OS數(shù)據(jù),在CPU端計算原始影像與糾正影像之間的單應變換關系,然后利用GPU并行計算實現(xiàn)影像的糾正過程；由于航空影像間具有較大重疊度,因此本文提出一種自適應的拼接方法,即通過計算后續(xù)影像的重疊度以判斷當前影像是否需要拼接,大大了減少冗余計算；同時本文利用兩臺計算機進行了模擬實驗,其中一臺作為模擬相機拍攝影像,往處理機器上傳輸影像。實驗結果表明,該方法基本實現(xiàn)了航空影像的在線實時拼接,且拼接結果滿足為災害、突發(fā)情況的實時救援提供決策信息需求。 本文研究所提出的配準和拼接方法采用Visual C++進行了實現(xiàn),并開發(fā)了相應原型系統(tǒng)。有關實驗結果表明本文方法能實現(xiàn)大尺寸遙感影像的高效配準以及航空影像的在線拼接。文中圖26個,表3個,參考文獻48篇。
[Abstract]:As a visual form of the energy or state of the objective world, image provides abundant information for people to recognize and transform the objective world. As one of the basic problems of image processing, image registration and image mosaic are widely used in the fields of virtual reality, geological survey, medical image, meteorological prediction, emergency response and so on. The graphics processor GPU uses more transistors as executive units, much more computing power than traditional central processor CPUs. At present, GPU technology has been widely used in the fields of data mining, mathematical statistics, image speech recognition, genetic engineering, accurate prediction of global climate and so on. At the same time, it provides a new solution for the rapid processing of remote sensing images. Aiming at the existing problems in image registration and stitching calculation, this paper analyzes the current image registration and stitching technology, and combines CUDA parallel computing technology. The registration method of large scale and high resolution remote sensing image and the online real-time mosaic method of aerial image are mainly studied in this paper. The main contents of this paper are as follows: (1) large scale remote sensing image registration. Aiming at the problem that the traditional remote sensing image registration method is difficult to adapt to the large scale and high resolution remote sensing image registration, this paper proposes a registration control point matching method from coarse to fine. The small panel differential correction method is used to realize the high accuracy correction of large scale image, and an adaptive scan line filling algorithm is used to calculate the triangle in which each corrected pixel is located. In this paper, by analyzing the bottleneck problem in each step of registration, the CUDA parallel computing technique is used to accelerate the control point matching and image correction. The experiments on IKONOS panchromatic and multispectral images show that the proposed method can achieve high registration accuracy and obtain a high speedup ratio by using GPU acceleration algorithm. (2) Real-time online stitching of aerial images. Aiming at the bottleneck of online real-time mosaic of aerial images, this paper presents an online stitching method based on CPU and GPU. Based on the POS data, the monoclinic transformation between the original image and the corrected image is calculated at the CPU end, and then the correction process of the image is realized by using the GPU parallel computation. In this paper, an adaptive stitching method is proposed, that is, by calculating the overlap degree of subsequent images to determine whether the current images need to be spliced, the redundant computation is greatly reduced, and at the same time, two computers are used to carry out simulation experiments. One of them takes the image as an analog camera and transmits the image to the processing machine. The experimental results show that the method can basically realize the online real-time mosaic of aerial images, and the results of the stitching can provide the decision information for the real-time rescue of disasters and emergencies. In this paper, the proposed registration and splicing methods are implemented with Visual C, and the corresponding prototype system is developed. The experimental results show that the proposed method can achieve the efficient registration of large scale remote sensing images and the online stitching of aerial images. There are 26 figures, 3 tables and 48 references.
【學位授予單位】：中南大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP751
本文編號：2154430
[Abstract]:As a visual form of the energy or state of the objective world, image provides abundant information for people to recognize and transform the objective world. As one of the basic problems of image processing, image registration and image mosaic are widely used in the fields of virtual reality, geological survey, medical image, meteorological prediction, emergency response and so on. The graphics processor GPU uses more transistors as executive units, much more computing power than traditional central processor CPUs. At present, GPU technology has been widely used in the fields of data mining, mathematical statistics, image speech recognition, genetic engineering, accurate prediction of global climate and so on. At the same time, it provides a new solution for the rapid processing of remote sensing images. Aiming at the existing problems in image registration and stitching calculation, this paper analyzes the current image registration and stitching technology, and combines CUDA parallel computing technology. The registration method of large scale and high resolution remote sensing image and the online real-time mosaic method of aerial image are mainly studied in this paper. The main contents of this paper are as follows: (1) large scale remote sensing image registration. Aiming at the problem that the traditional remote sensing image registration method is difficult to adapt to the large scale and high resolution remote sensing image registration, this paper proposes a registration control point matching method from coarse to fine. The small panel differential correction method is used to realize the high accuracy correction of large scale image, and an adaptive scan line filling algorithm is used to calculate the triangle in which each corrected pixel is located. In this paper, by analyzing the bottleneck problem in each step of registration, the CUDA parallel computing technique is used to accelerate the control point matching and image correction. The experiments on IKONOS panchromatic and multispectral images show that the proposed method can achieve high registration accuracy and obtain a high speedup ratio by using GPU acceleration algorithm. (2) Real-time online stitching of aerial images. Aiming at the bottleneck of online real-time mosaic of aerial images, this paper presents an online stitching method based on CPU and GPU. Based on the POS data, the monoclinic transformation between the original image and the corrected image is calculated at the CPU end, and then the correction process of the image is realized by using the GPU parallel computation. In this paper, an adaptive stitching method is proposed, that is, by calculating the overlap degree of subsequent images to determine whether the current images need to be spliced, the redundant computation is greatly reduced, and at the same time, two computers are used to carry out simulation experiments. One of them takes the image as an analog camera and transmits the image to the processing machine. The experimental results show that the method can basically realize the online real-time mosaic of aerial images, and the results of the stitching can provide the decision information for the real-time rescue of disasters and emergencies. In this paper, the proposed registration and splicing methods are implemented with Visual C, and the corresponding prototype system is developed. The experimental results show that the proposed method can achieve the efficient registration of large scale remote sensing images and the online stitching of aerial images. There are 26 figures, 3 tables and 48 references.
【學位授予單位】：中南大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP751
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