基于图像的虚拟场景绘制关键技术研究
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摘要
可交互性、沉浸感和想象力一直是虚拟现实系统(VR系统)所追求的目标，而真实感和实时性是VR系统构建中的一对矛盾。传统基于几何的虚拟场景绘制技术是以计算机图形学为基础，可以准确获知场景的深度信息，因而用户与场景对象之间具有良好的交互性，且视点可以自由变换。但该方法的复杂度完全依赖于场景，限制了其在普通硬件平台上应用。基于图像的绘制方法将图像作为信息载体，通过对原图像的重采样、重组织的绘制过程，因而具有绘制速度独立于场景复杂度、真实感强等特点，并成为近年来虚拟现实技术的研究热点。
本文以基于图像绘制的虚拟场景生成为研究主线，主要研究了图像拼接技术、基于图像的重光照技术、图像分辨率增强技术等关键技术。主要研究内容如下：
图像拼接是虚拟场景建模的主要技术之一，针对光照变化图像序列提出了两种基于特征的图像拼接算法：基于静态小波序贯特征匹配和同态滤波的图像拼接算法以及基于圆投影和能量函数优化的图像拼接算法。其中基于静态小波序贯特征匹配和同态滤波的图像拼接算法采用静态小波提取图像匹配模板的特征序列，较好地实现了光照不同的图像匹配；而对于初始图像获取时光照条件的不同，采用同态滤波等技术解决初始图像的光照不一致问题。基于圆投影和能量函数优化的图像拼接算法则采用圆投影提取图像匹配模板的特征序列，克服了传统图像特征提取方法中的区域局限问题；对于图像融合，论文从图像能量角度出发，构造一个包含图像梯度的能量泛函，通过最小化该函数来求取图像重叠区域的全局最优融合因子，最终实现快速、准确地拼接图像。
研究了光照条件对于场景图像的重要意义，提出了一种基于图像的重光照算法。该算法在给定目标若干基图像及其对应光照属性的基础上，通过基图像数据矩阵的奇异值分解(SVD)分离场景的环境光分量、反演场景的表面反射率和表面法向量等。新光照条件下场景的绘制，直接光照采用光照模型直接计算；间接光照采用分离出的基图像环境光分量拟合，较好地实现了变化光照下场景图像的生成。
针对高分辨率真实感虚拟环境及场景浏览时变焦观察的需要，研究了图像分辨率增强算法。对于单帧图像的分辨率增强，提出一种基于熵变分的图像分辨率增强算法。该算法在贝叶斯估计和最大熵原理的基础上，将图像象素点梯度信息应用到图像分辨率增强中，从而建立起一种基于图像梯度信息的各向异性自适应分辨率增强算法。对于多帧图像序列的超分辨率复原，在单帧熵变分模型的基础上，将双边滤波技术引入到图像超分辨率复原中，建立了一种基于广义熵变分的图像超分辨率复原模型，从而提出一种基于几何距离和梯度信息的双重加权各向异性分辨率增强算法。实验表明：使用本文算法得到的超分辨率复原图像具有较高的峰值信噪比和视觉质量。
针对基于图像的虚拟场景虚化关联的需要，将非真实感绘制技术引入虚拟场景中，提出了一种基于线积分卷积的场景虚化关联生成方法。该方法通过计算结构张量获得图像曲线走向矢量场，以此刻画图像局部结构；并采用全变分偏微分方程对曲线走向矢量场自适应滤波平滑处理；在此基础上利用线积分卷积指导画笔走向模拟生成线条波动感虚化效果图像；最后结合原始真实图像和虚化效果图像，实现了一种基于时间变量的柱面场景虚化关联算法，并为虚拟现实系统超越现实时空的需要提供了一种新思路。
针对基于图像的虚拟全景空间漫游的需要，在定义位置链、时间链、焦距链和虚化链等链表结构的基础上，提出了一种包含位置关联、时间关联、焦距关联和虚化关联的大规模虚拟全景空间漫游策略。
Interactive, immersion, and imagination are the objectives for virtual reality system, while reality and realtime are contradicted during VR system construction. Traditional virtual scene coustruction method based on geometry is on the basis of computer graphics; the depth information of scene can be obtained directly, which offers a convenient interactive mode between user and the scene. Also, user can change their viewpoint freely. However complexity of this method is entirely depends on virtual scene, and it is difficult to realize on common harder platform. A completely brandnew method-image based rendering is proposed recently, which uses image as the carrier of information. This method uses pixel as rendering element, rendering is just the process of resample and reorganize former pixel. Advantages of this method are rendering speed independent of scene complexity and high fidelity. So it becomes a hot reseach spot in virtual reality field.
This dissertation concentrates on the key technology of virtual scene rendering based on image. Aimed at the needs of virtual panoramic space construction, we emphatic research the image stitching method; image based relighting method and image resolution enhancement method. Generally, the main research points in this paper are as follows:
Image Stitching is one of the key technology in virtual reality construction,we proposes two ambient light independent panorama mosaics methods based on feature: image stitching algorithm based on stationary wavelet decomposition and homomorphic filtering, and image stitching algorithm based on ring projection and energy function optimization. The first algorithm uses stationary wavelet decomposition to extract feature sequences in image templates. Then color transform or homomorphic filtering can be used to adjust the global brightness of images to solve the problem of difference illumination, thus a pleasure stitching result can be achived. The second algorithm uses ring projection to extract feature sequences in image templates, which overpasses the local area limit in tradition methods. While blending factors are gained from energy function that contains image gradient, which conquer the blur and mackle problems in the overlap area using tradition linear weight function. Thus we can gain pleasure visual effect using these two methods in image mosaics with notable illumination difference.
Illumination is an important factor for scene generation, we proposes a pure image based rendering method to synthesis image under arbitrary illumination condition. In this method, Singular Value Decomposition (SVD) was used analytically to derive environment light part and properties of scenes from sampled images, which were taken from the same view point under different point illumination condition. For images rendering under novel lighting conditions, direct illumination effect is calculated using lambertain model; while indirect illumination one was added by environment light part derived before. So feature of this method is that it supplies a good algorithm to synthesis image in arbitrary illumination environment.
Aimed at the needs of high image resolution and zooming browse in virtual environment, this paper research algorithm of image resolution enhancement. In the case of one frame, we presented a novel image zooming algorithm based on maximum a posteriori (MAP) probability theory and maximum entropy principle, which anisotropy interpolate pixels using gradient information self-adaptively. In the case of multi-frames, we presented a super-resolution reconstruction algorithm based on generalized MAP theory and maximum entropy principle. By introduce bifilter into traditional MAP algorithm, we anisotropy interpolate pixels using gradient information and geometry distance self-adaptively. Experiment shows that our algorithm holds higher signal/noise ratio and better vision quality.
Aimed at the needs of virtualized relationship in virtual environment, we introduce non-photorealistic rendering into virtual reality and presented a novel algorithm for virtualized relationship generation in image based virtual environment based on line integral convolution. First, structure tensor is calculated to gain vector field for characterizing local structure of image. And then, total variation partial difference equation is imposed for smoothing vector field. Based on this we use line integral convolution for guide the stroke to generate virtual special effect image with fluctuant sense. Last, mergering with original real image, cylindrical panorama with virtualized relationship controlled by time can be generated, which accomplished the character of surpass the time and space in virtual reality system.
Aimed at the needs of divagation in virtual environment, we define the position chain, time chain, zooming chain and virtulized chain. Also, a divagation strategy is proposed for vitural panorama space containing position relationship, time relationship, zooming relationship and virtualized relationship.
本文以基于图像绘制的虚拟场景生成为研究主线，主要研究了图像拼接技术、基于图像的重光照技术、图像分辨率增强技术等关键技术。主要研究内容如下：
图像拼接是虚拟场景建模的主要技术之一，针对光照变化图像序列提出了两种基于特征的图像拼接算法：基于静态小波序贯特征匹配和同态滤波的图像拼接算法以及基于圆投影和能量函数优化的图像拼接算法。其中基于静态小波序贯特征匹配和同态滤波的图像拼接算法采用静态小波提取图像匹配模板的特征序列，较好地实现了光照不同的图像匹配；而对于初始图像获取时光照条件的不同，采用同态滤波等技术解决初始图像的光照不一致问题。基于圆投影和能量函数优化的图像拼接算法则采用圆投影提取图像匹配模板的特征序列，克服了传统图像特征提取方法中的区域局限问题；对于图像融合，论文从图像能量角度出发，构造一个包含图像梯度的能量泛函，通过最小化该函数来求取图像重叠区域的全局最优融合因子，最终实现快速、准确地拼接图像。
研究了光照条件对于场景图像的重要意义，提出了一种基于图像的重光照算法。该算法在给定目标若干基图像及其对应光照属性的基础上，通过基图像数据矩阵的奇异值分解(SVD)分离场景的环境光分量、反演场景的表面反射率和表面法向量等。新光照条件下场景的绘制，直接光照采用光照模型直接计算；间接光照采用分离出的基图像环境光分量拟合，较好地实现了变化光照下场景图像的生成。
针对高分辨率真实感虚拟环境及场景浏览时变焦观察的需要，研究了图像分辨率增强算法。对于单帧图像的分辨率增强，提出一种基于熵变分的图像分辨率增强算法。该算法在贝叶斯估计和最大熵原理的基础上，将图像象素点梯度信息应用到图像分辨率增强中，从而建立起一种基于图像梯度信息的各向异性自适应分辨率增强算法。对于多帧图像序列的超分辨率复原，在单帧熵变分模型的基础上，将双边滤波技术引入到图像超分辨率复原中，建立了一种基于广义熵变分的图像超分辨率复原模型，从而提出一种基于几何距离和梯度信息的双重加权各向异性分辨率增强算法。实验表明：使用本文算法得到的超分辨率复原图像具有较高的峰值信噪比和视觉质量。
针对基于图像的虚拟场景虚化关联的需要，将非真实感绘制技术引入虚拟场景中，提出了一种基于线积分卷积的场景虚化关联生成方法。该方法通过计算结构张量获得图像曲线走向矢量场，以此刻画图像局部结构；并采用全变分偏微分方程对曲线走向矢量场自适应滤波平滑处理；在此基础上利用线积分卷积指导画笔走向模拟生成线条波动感虚化效果图像；最后结合原始真实图像和虚化效果图像，实现了一种基于时间变量的柱面场景虚化关联算法，并为虚拟现实系统超越现实时空的需要提供了一种新思路。
针对基于图像的虚拟全景空间漫游的需要，在定义位置链、时间链、焦距链和虚化链等链表结构的基础上，提出了一种包含位置关联、时间关联、焦距关联和虚化关联的大规模虚拟全景空间漫游策略。
Interactive, immersion, and imagination are the objectives for virtual reality system, while reality and realtime are contradicted during VR system construction. Traditional virtual scene coustruction method based on geometry is on the basis of computer graphics; the depth information of scene can be obtained directly, which offers a convenient interactive mode between user and the scene. Also, user can change their viewpoint freely. However complexity of this method is entirely depends on virtual scene, and it is difficult to realize on common harder platform. A completely brandnew method-image based rendering is proposed recently, which uses image as the carrier of information. This method uses pixel as rendering element, rendering is just the process of resample and reorganize former pixel. Advantages of this method are rendering speed independent of scene complexity and high fidelity. So it becomes a hot reseach spot in virtual reality field.
This dissertation concentrates on the key technology of virtual scene rendering based on image. Aimed at the needs of virtual panoramic space construction, we emphatic research the image stitching method; image based relighting method and image resolution enhancement method. Generally, the main research points in this paper are as follows:
Image Stitching is one of the key technology in virtual reality construction,we proposes two ambient light independent panorama mosaics methods based on feature: image stitching algorithm based on stationary wavelet decomposition and homomorphic filtering, and image stitching algorithm based on ring projection and energy function optimization. The first algorithm uses stationary wavelet decomposition to extract feature sequences in image templates. Then color transform or homomorphic filtering can be used to adjust the global brightness of images to solve the problem of difference illumination, thus a pleasure stitching result can be achived. The second algorithm uses ring projection to extract feature sequences in image templates, which overpasses the local area limit in tradition methods. While blending factors are gained from energy function that contains image gradient, which conquer the blur and mackle problems in the overlap area using tradition linear weight function. Thus we can gain pleasure visual effect using these two methods in image mosaics with notable illumination difference.
Illumination is an important factor for scene generation, we proposes a pure image based rendering method to synthesis image under arbitrary illumination condition. In this method, Singular Value Decomposition (SVD) was used analytically to derive environment light part and properties of scenes from sampled images, which were taken from the same view point under different point illumination condition. For images rendering under novel lighting conditions, direct illumination effect is calculated using lambertain model; while indirect illumination one was added by environment light part derived before. So feature of this method is that it supplies a good algorithm to synthesis image in arbitrary illumination environment.
Aimed at the needs of high image resolution and zooming browse in virtual environment, this paper research algorithm of image resolution enhancement. In the case of one frame, we presented a novel image zooming algorithm based on maximum a posteriori (MAP) probability theory and maximum entropy principle, which anisotropy interpolate pixels using gradient information self-adaptively. In the case of multi-frames, we presented a super-resolution reconstruction algorithm based on generalized MAP theory and maximum entropy principle. By introduce bifilter into traditional MAP algorithm, we anisotropy interpolate pixels using gradient information and geometry distance self-adaptively. Experiment shows that our algorithm holds higher signal/noise ratio and better vision quality.
Aimed at the needs of virtualized relationship in virtual environment, we introduce non-photorealistic rendering into virtual reality and presented a novel algorithm for virtualized relationship generation in image based virtual environment based on line integral convolution. First, structure tensor is calculated to gain vector field for characterizing local structure of image. And then, total variation partial difference equation is imposed for smoothing vector field. Based on this we use line integral convolution for guide the stroke to generate virtual special effect image with fluctuant sense. Last, mergering with original real image, cylindrical panorama with virtualized relationship controlled by time can be generated, which accomplished the character of surpass the time and space in virtual reality system.
Aimed at the needs of divagation in virtual environment, we define the position chain, time chain, zooming chain and virtulized chain. Also, a divagation strategy is proposed for vitural panorama space containing position relationship, time relationship, zooming relationship and virtualized relationship.
引文
[1]．张茂军．虚拟现实系统[M]．科学出版社，2001．
[2]．石教英．虚拟现实基础及实用算法[M]．科学出版社，2002．
[3]. Szeliski R. Video mosaics for virtual environments[J]. IEEE Computer Graphics and Applications. 1996, 16(2): 16-20.
[4]. Kuglin, Hines. The phase correction image alignment method[C]. Proceedings of IEEE International Conference on Cybernetices and Society. 1975: 163-165.
[5]. S A. Spatial registration of multispectral and multitemporal digital imagery using fast-fourier transform techniques[J]. IEEE Transaction on Pattem Analysis and Machine Intelligence. 1993, 15(5): 499-504.
[6]. S A., C M. Image registration using projections[J]. IEEE Transaction on Pattern Analysis and Machine Intelligence. 1986, 8(2): 222-233.
[7]. Kim D.-H., Kim s.-H., Choi J.-S. Forward-view transition algorithm between the near and the far views[J]. Optical Engineer. 2004, 43(12): 3104-3113.
[8]. Reddy B.S., Chatterji B.N. An fft-based technique for transaction, rotation, and scale invariant image registration[J]. IEEE Transaction on Image Processing. 1996, 5(8): 1266-1271.
[9]．李忠新，茅耀斌，王执铨．基于对数极坐标映射的图像拼接方法[J]．中国图象图形学报．2005，10(1)：59-63．
[10]. Z. Zhang, R. Deriche, O. T. Luong. A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry[J]. Artical Intelligence. 1995, 78(1): 87-119.
[11]. Peng D., Liu J., Tian J., Zheng S. Transformation model estimation of image registration via least square support vector machines[J]. Pattem Recognition Letters. 2006, 27: 1397-1404.
[12]. T H. C., JaLing W. Multiresolution mosaic[J]. IEEE Transaction Consumer Electronics. 1996, 42(3): 981-990.
[13]．蒋晓瑜，黄应清．基于小波变换的多分辨模板匹配[J]．中国图象图形学报．2000，5(4)：304-308．
[14]．罗钟铉，刘成明．灰度图像匹配的快速算法[J]．计算机辅助设计与图形学学报．2005，17(5)：966-970．
[15]. MingShing S., L H.W., Young C.K. Analysis on multiresolution mosaic images[J]. IEEE Transactions On image processing. 2004, 13(7): 952-959.
[16]. Levin A., Zomet A., Peleg S., Weiss Y. Seamless image stitching in the gradient domain[C]. Proceedings of European Conference on Computer Vision. 2004: 377-389.
[17]. K. Jung, S. Lacroix. Robust interest points matching algorithm[C]. Proceedings of International Conference on Computer Vision. Canada, 2001: 538-543.
[18]. Q. Wu, Y. Yu. Matching and deformation for texture synthesis[C]. Proceedings of Proceeding of Computer Graphics. Los Angles, 2004: 362-365.
[19]. K. Gruman, T. Darrell. Efficient image matching with distributions of local invariant features[C]. Proceedings of Computer Vision and Pattern Recognition. Canada, 2005: 627-634.
[20]. Castleman K. R. Digital image processing[M]. Englewood Cliffs: Prentice-Hall International Inc, 1996.
[21]. A. Kaplan, E. Rivlin, I. Shimshoni. Robust feature matching across widely separated color images[C]. Proceedings of 17th International Conference on Pattern Recognition. 2004: 774-781.
[22]．全红艳，张田文，蔺想红．全景图拼接中的一种快速图像点对应策略[J]．机器人．2005，27(3)：236-240．
[23]．仵建宁，郭宝龙，冯宗哲．一种基于兴趣点特征匹配的图像镶嵌技术[J]．光电子·激光．2006，17(6)：733-737．
[24]. Siu A. M. K., Lau R. W. H. Image registration for image-based rendering[J]. IEEE Transaction on Image Processing. 2005, 14(2): 241-252.
[25]. Bouchafa S., Zavidovique B. Efficient cumulative matching for image registration[J]. Image and Vision Computing. 2006, 24: 70-79.
[26]. Jagannadan V., Prakash M. C., Sarma R. R., Rao G. V. P. Feature extraction and image registration of color images using fourier bases[C]. Proceedings of IEEE International Conference on Image Processing. 2005: 662-665.
[27]. Cordon O., Damas S., Santamaria J. Feature-based image registration by means of the chc evolutionary algorithm[J]. Image and Vision Computing. 2006, 24: 525-533.
[28]. Lazaridis G., Petrou M. Image registration using the waish transform[J]. IEEE Transaction on Image Processing. 2006, 15(8): 2343-2357.
[29]．封靖波，苏志勋，刘秀平．一种基于相似曲线的全景图自动拼接算法[J]．计算机学报．2003，26(11)：1604-1608．
[30]．沈沉，沈向洋，马颂德．基于图像的光照模型研究综述[J]．计算机学报．2000，23(12)：1261-1269．
[31]. Debevec P. Image based lighting[J]. IEEE Computer Graphics and Applications. 2002, 22(2): 26-34.
[32]. Agusanto K., Li L., Chuangui Z., Sing N.W. Photorealistic rendering for augmented reality using environment illumination[C]. Proceedings of The Second IEEE and ACM International Symposium on Mixed and Augmented Reality Singapore, 2003: 208-216.
[33]. Park Y., Choi T., Yoon K. Brdf recovering and scene re-lighting using an environment map[C]. Proceedings of International Conference on Computer Graphics, Imaging and Vision. China, 2005: 310-314.
[34]. Jensen T., Andersen M. S., Madsen C. B. Real-time image based lighting for outdoor augmented reality under dynamically changing illumination conditions[C]. Proceedings of International Conference on Computer Graphics Theory and Applications Portugal 2006: 323-330.
[35]. Nishino K., Sato Y., Ikeuchi K. Eigen-texture method: Appearance compression based on 3d model[C]. Proceedings of Proceedings of IEEE Conference on Computer Vision and Pattern Recognition 1999: 618-624.
[36]. Wong T.T., Heng P. A., Or S. h., Ng W. Y. Image-based rendering with controllable illumination[C]. Proceedings of Proceedings of the Eurographics Workshop on Rendering Techniques 1997: 13-22.
[37]. Shashua A., Riklin-Raviv T. The quotient image: Class-based re-rendering and recognition with varying illuminations[J]. IEEE Transaction on Pattern Analysis and Machine Intelligence. 2001, 25(2): 129-139.
[38]. Mukaigawa Y. Photometric image-based rendering for image generation in arbitrary illumination[C]. Proceedings of Proceedings of ICCV. 2001: 652-659.
[39]. Mukaigawa A. Photometric image-based rendering for virtual lighting image synthesis[C]. Proceedings of 2nd IEEE and ACM International Workshop on Augmented Reality. 1999: 115-124.
[40]. Jim B., NewellMartin. Texture and reflection in computer generated images[J]. Communications of ACM. 1976, 19(10): 542-547.
[41]. Yung-Yu C., E Z. D., J H. i. r. Environment matting extensions: Towards higher accuracy and realtime capture[C]. Proceedings of SIGGRAPH. New Orleans, 2000: 121-130.
[42]. Dan X., Pheng-ann H. Object image synthesis under changing of illumination[J]. Journal of Software. 2002, 13(4): 501-509.
[43]．姜忠鼎．基于深度全景视频的虚拟场景绘制技术研究[D]．浙江大学，2004
[44]. W.Freeman, T.Jones, E.Pasztor. Example-based super-resolution[J]. IEEE Computer Graphics and Applications. 2002, 22(2): 56-65.
[45]. X.Debevee, L.McMillan. Image-based modeling,rendering and lighting[J]. IEEE Computer Graphics and Applications. 2002, 22(2): 24-25.
[46]．孙庆杰，张晓鹏，吴恩华．一种基于bezier插值曲面的图像放大方法[J]．软件学报．1999，10(6)：570-574．
[47]．谢美华，王正明．图像分辨率增强的偏微分方程方法[J]．遥感学报．2005，9(6)：673-679．
[48]．朱宁，吴静，王忠谦．图像放大的偏微分方程方法[J]．计算机辅助设计与图形学学报．2005，17(9)：1941-1945．
[49]．逯峰．Tikhonov正则化方法在带噪数字图像缩放中的应用[J]．计算机辅助设计与图形学学报．2002，14(7)：651-654．
[50]．肖亮，吴慧中，韦志辉，汤淑春，刘扬．基于图的数字全变差模型及其带噪图像任意精度放大[J]．计算机辅助设计与图形学学报．2004，16(1)：51-57．
[51]．陶洪久，饶俊飞，周祖德．单幅图像的超分辨率重建方法[J]．武汉理工大学学报．2004，28(6)：943-946．
[52]. R.Y.Tsai, A.K.Huang. Multiframe image restoration and registration[C]. Proceedings of in Advances in Computer Vision and Registration. 1984: 317-339.
[53]. S B. Topics in multiframe superresolution restoration[D]. University of Notre Dame, 2004
[54]. Zomet A., Peleg S. Efficient super-resolution and applications to mosaics[C]. Proceedings of Proceeding of the Internation Conference on Pattern Recognition. 2000: 579-583.
[55]．谭兵，徐青，耿则勋，邢帅．超分辨率图象重建技术发展现状[J]．信息工程大学学报．2003，4(4)：93-97．
[56]. Tekalp A.M., KOzkan M., Sezan M.L. High resolution image reconstruction for lower-resolution image sequences and space-varying image restoration[C]. Proceedings of Proceedings of the IEEE International Conference on Acousics, Speech and Signal Proceeding on Acousics, Speech and Signal Processing. 1992: 169-172.
[57]. E. Kaltenbacher, R. C. Hadie. High resolution infrared image reconstruction using multiple low-resolution aliased frames[C]. Proceedings of IEEE International Conference on National Aerospace and Electronic. 1996: 702-709.
[58]. D.Keren, S.Peleg, R.Brada. Image sequence enhancement using subpixel displacements[C]. Proceedings of Proceedings of The IEEE Computer Vision and Pattern Recognition. 1998: 742-746.
[59]. M.Irani, S.peleg. Improving resolution by image registration[J]. CVGIP: Graphical Models and Image Processing 1991, 53(3): 231-239.
[60]. Tom B.C., A.K.Katsaggelos. Resolution enhancement of video sequences using motion compensation[C]. Proceedings of Proceedings of the IEEE International Conference on Image Processing. 1996: 713-716.
[61]. Messina G., Battiato S., Mancuso M., Buemi A. Improving image resolution by adaptive back-projection correction[J]. IEEE Transactions on Consumer Electronics. 2002, 48(3): 409-416.
[62]．黄华，樊鑫，齐春，朱世华．基于识别的凸集投影人脸图像超分辨率重建[J]．计算机研究与发展．2005，42(10)：1718-1725．
[63]. Ding J.Z., Jun B.H. A super-resolution method with ewa[J]. Journal of Computer Science and Technology. 2003, 18(6): 822-832.
[64]. R.Schultz R., L.Stevenson R. Improved difinition image expansion[C]. Proceedings of Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. 1992: 173-176.
[65]. Gunturk B.K., Gevrekci M. High-resolution image reconstruction from multiple differently exposed images[J]. IEEE Signal Processing Letters. 2006, 13(4): 197-200.
[66]. Farsiu S., Elad M., Milanfar P. Multiframe demosaicing and super-resolution of color images[J]. IEEE Transaction on Image Processing. 2006, 15(1): 141-160.
[67]. Farsiu S., Robinson D., Elad M., Milanfar P. Robust shift and add approach to super-resolution[C]. Proceedings of SPIE Conference on Applic Digital Signal and Image Process. 2003: 121-130.
[68]．韩玉兵，吴乐南．基于自适应滤波的视频序列超分辨率重建[J]．计算机学报．2006，29(4)：642-647．
[69]. Binghua S., Weiqi J. Pocs-mpmap based super-resolution image restoration[J]. ACTA PHOTONICA SINICA. 2003, 32(4): 502-504.
[70]. Yu Y., Sun Y.L., Liu H.-J. Steepest gradient descent method for estimation of motion parameters from image sequences[C]. Proceedings of International Symposium on Video Communication and Fiber Optic Networks. 1993: 297-301.
[71]. W.Freeman, E.Pasztor, O.Camichaei. Learning low-level vision[J]. International Journal of Computer Vision. 2000, 40(1): 25-47.
[72]. J.Sun, Zheng N., H.Tao, Shum H.Y. Image hallucination with primal sketch priors[C]. Proceedings of Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2003: 729-736.
[73]. E H., L T. Learning regularization functionals—a supervised training approach[J]. Inverse Problems. 2003, 19(3): 611-626.
[74]. Chappalli M.B., Bose N.K. Simultaneous noise filtering and super-resolution with second-generation wavelets[J]. IEEE Signal Processing Letters. 2005, 12(11): 772-775.
[75]. H K., KS H. Variational approaches to super-resolution with contrast enhancement and anisotropic diffusion[J]. Journal of Electronic Imaging. 2003, 12(1): 244-251.
[76]. S F., MD R. Fast and robust multiframe super resolution[J]. IEEE Transactions on Image Processing. 2004, 13(10): 1327-1344.
[77]. D B. Bilateral filtering and anisotropic diffusion: Towards a unified viewpoint[R]. Hewlett-Packard Laboratories Technical Report, 2000
[78]. M E. On the bilateral filter and ways to improve it[J]. IEEE Transactions on Image Processing. 2002, 11(10): 1141-1151.
[79]. V.M.Joshi, S.Chaudhuri, R.Panuganti. A learning-based method for image super-resolution from zoomed observations[J]. IEEE Transaction on Systems,man and cybernetics. 2005, 35(3): 527-537.
[80]. He H., Kondi L.P. An image super-resolution algorithm for different error levels per frame[J]. IEEE Transactions On Image Processing. 2006, 15(3): 592-603.
[81]. Robinson D., Milanfar P. Statistical performance analysis of super-resolution[J]. IEEE Transaction On Image Processing. 2006, 15(6): 1413-1428.
[82]．张新明，沈兰荪．基于多尺度边缘保持正则化的超分辨率复原[J]．软件学报．2003，14(6)：1075-1081．
[83]．孟庆武．预估计混叠度的map超分辨率处理算法[J]．软件学报．2004，15(2)：207-214．
[84]．邵文泽，韦志辉．基于广义huber-mrf图像先验模型的超分辨率复原算法[J]．软件学报．2006，17(12)．
[85]. R H., R G. Linear push broom-cameras[C]. Proceedings of International Proceedings of the 3rd European Conference on computer Vision. 1994: 555-566.
[86]. Jang K.H., Jung S.K. Constructing cylindrical panoramic image using equidistant matching[J], IEE Electronics Letters. 1999, 19(8): 1715-1716.
[87]．漆驰，刘强，孙家广．摄像机图像序列的全景图拼接[J]．计算机辅助设计与图形学学报．2001，13(7)：605-609．
[88]．周骥，石教英，赵友兵．图像特征点匹配的强壮算法[J]．计算机辅助设计与图形学学报．2002，14(8)：754-758．
[89]．岳焕印，郭华东，王长林．Sar干涉图的静态小波域map法滤波[J]．遥感学报．2002，6(6)：456-463．
[90]. J S.M. The discrete wavelet transform: Wedding the atrous and mallat algorithms[J]. IEEE Transactions on Signal Processing. 1992, 40(10): 2464-2482.
[91]．吴艳，李明，梁继民．一种图像融合新算法[J]．电子与信息学报．2003，25(1)：44-49．
[92]．章毓晋．图像工程[M]．清华大学出版社，1999．
[93]. Erik R., Michael A., Bruce G. Color transform between images[J]. IEEE Computer Graphics and Applications. 2001, 21(5): 34-41.
[94]．赵国英，向世明，李华．高阶矩在颜色传输中的应用[J]．计算机辅助设计与图形学学报．2004，16(1)：62-68．
[95]. Y.Tang Y., Liu J., Li B.F., Xi D. Multiresolution analysis in extraction of reference lines from documents with gray level background[J]. IEEE Transaction on Pattern Analysis and Machine Intelligence. 1997, 19(8): 921-926.
[96]. Tang Y.Y., Cheng H.D., Suen C.Y. Transformation-ring projection (trp) algorithm and its visi implementation[J]. Internation Pattern Recognition Artificial Intelligence. 1991, 5(12): 25-36.
[97]. L M., G B. Plenoptic modeling: An image-based rendering system[C]. Proceedings of Proceedings of the SIGGRAPH 1995: 39-46.
[98]．彭群生，鲍虎军，金小刚．计算机真实感图形的算法基础[M]．北京：科学出版社，1999．
[99]. Epstein R., Yuille A.L., Belhumeur P.N. Learning object representations from lighting variations.[C]. Proceedings of Proceedings of the Internelationai Workshop on Object Representation for Computer Vision. 1996:
[100]．张贤达．矩阵分析与应用[M]．北京：清华大学出版社，2004．
[101]. Http://cvc.Yale.Edu/people/faculty/belhumeur.Html. 2006.
[102]. Krim H., Hamza A.B. Towards a unified view of estimation: Variation vs. Statistica[C]. Proceedings of IEEE International conference on Image Processing. 2001: 577-580.
[103]. E. T J. On the rationale of maximum-entropy methods[C]. Proceedings of Proceedings of the IEEE. 1982: 939-952.
[104]. N S., R K., R M. A geometrical framework for low-level vision[J]. IEEE transaction on image processing. 1998, 7(3): 310-318.
[105]. P B., T. F C. Color tv: Total variation methods for restoration of vector-valued images[J]. IEEE Transaction On Image Processing. 1998, 7(3): 304-309.
[106]. Chan T. F., Osher S., Shen J. The digital tv filter and nonlinear denoising[J]. IEEE Transaction On Image Processing. 2001, 10(2): 231-241.
[107]．孙立峰，钟力，李云浩，胡晓峰．虚拟实景空间的实时漫游[J]．中国图象图形学报．1999，4(6)：507-513．
[108]．刘建威，崔杜武．基于全景图的多视点虚拟空间漫游技术[J]．计算机工程．2004，30(1)：153-154．
[109]. W. Klein A. An image-based framework for animated non-photorealistic rendering[D]. Princeton University, 2002
[110]. Curtis, S.Anderson, Seims J., Fleischer K., D.Salesin. Computer-generated watercolor[C]. Proceedings of Annual Conference Series, ACM SIGGRAPH. 1997: 421-430.
[111]. Hertzmann A., Jacobs C. E., Oliver N., Curless B., Salesin D. H. Image analogies[C]. Proceedings of SIGGRAPH 2001 Conference Proceedings. 2001: 327-340.
[112]. Cabral B., Leedom L. Imaging vector fields using line integral convolution[C]. Proceedings of Annual Conference Series, ACM SIGGRAPH. 1993: 263-272.
[113]. Wang C.-M., Lee J.-S. Using ILIC algorithm for an impressionist effect and stylized virtual environments[J]. Journal of Visual Languages and Computing. 2003, 14(3): 255-274.
[114]. Wang C.-M., Lee J.-S. Non-photorealistic rendering for aesthetic virtual environments[J]. Journal of Information Science and Engineering. 2004, 20(5): 923—948.
[115]. Kothe U. Edge and junction detection with an improved structure tensor[C]. Proceedings of DAGM Symposium Symposium for Pattern Recognition. 2003: 25-32.
[116]．程建刚，田捷，何余良，杨鑫．基于非线性扩散滤波的指纹增强算法[J]．自动化学报．2004，30(6)：854-862．
[117]. A. Aly H., Dubois E. Image up-sampling using total-variation regularization with a new observation model[J]. IEEE Transactions on Image Processing. 2005, 14(10): 1647-1659.
[118]. L R., S O., E F. Nonlinear total variation based noise removel algorithms[J]. Physical D: Nonlinear Phenomena. 1992, 10(60): 259-268.
[119]. Dasgupta S., Banerjee A. An augmented-reality-based real-time panoramic vision system for autonomous navigation[J]. IEEE Transaction on Systems,man and cybernetics. 2006, 36(1): 154-161.
[120]. Dasgupta S., Banerjee A. A real-time panoramic vision system for autonomous navigation[C]. Proceedings of Proceedings of the 36th conference on Winter simulation Washington, D. C, 2004: 1706-1712
[121]. Endo T., Katayama A., Tamura H., Hirose M., Tanikawa T., Saito M. Image-based walk-through system for large-scale scenes[C]. Proceedings of Proceeding of Virtual Systems and Multi-media. JIFU,Japan 1998: 269-274.
[122]. Bauermann I., Steinbach E. An image-based scene representation and rendering framework incorporating multiple representations[C]. Proceedings of Vision Modeling and Visualization Munich, Germany, 2003: 103-110.
[2]．石教英．虚拟现实基础及实用算法[M]．科学出版社，2002．
[3]. Szeliski R. Video mosaics for virtual environments[J]. IEEE Computer Graphics and Applications. 1996, 16(2): 16-20.
[4]. Kuglin, Hines. The phase correction image alignment method[C]. Proceedings of IEEE International Conference on Cybernetices and Society. 1975: 163-165.
[5]. S A. Spatial registration of multispectral and multitemporal digital imagery using fast-fourier transform techniques[J]. IEEE Transaction on Pattem Analysis and Machine Intelligence. 1993, 15(5): 499-504.
[6]. S A., C M. Image registration using projections[J]. IEEE Transaction on Pattern Analysis and Machine Intelligence. 1986, 8(2): 222-233.
[7]. Kim D.-H., Kim s.-H., Choi J.-S. Forward-view transition algorithm between the near and the far views[J]. Optical Engineer. 2004, 43(12): 3104-3113.
[8]. Reddy B.S., Chatterji B.N. An fft-based technique for transaction, rotation, and scale invariant image registration[J]. IEEE Transaction on Image Processing. 1996, 5(8): 1266-1271.
[9]．李忠新，茅耀斌，王执铨．基于对数极坐标映射的图像拼接方法[J]．中国图象图形学报．2005，10(1)：59-63．
[10]. Z. Zhang, R. Deriche, O. T. Luong. A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry[J]. Artical Intelligence. 1995, 78(1): 87-119.
[11]. Peng D., Liu J., Tian J., Zheng S. Transformation model estimation of image registration via least square support vector machines[J]. Pattem Recognition Letters. 2006, 27: 1397-1404.
[12]. T H. C., JaLing W. Multiresolution mosaic[J]. IEEE Transaction Consumer Electronics. 1996, 42(3): 981-990.
[13]．蒋晓瑜，黄应清．基于小波变换的多分辨模板匹配[J]．中国图象图形学报．2000，5(4)：304-308．
[14]．罗钟铉，刘成明．灰度图像匹配的快速算法[J]．计算机辅助设计与图形学学报．2005，17(5)：966-970．
[15]. MingShing S., L H.W., Young C.K. Analysis on multiresolution mosaic images[J]. IEEE Transactions On image processing. 2004, 13(7): 952-959.
[16]. Levin A., Zomet A., Peleg S., Weiss Y. Seamless image stitching in the gradient domain[C]. Proceedings of European Conference on Computer Vision. 2004: 377-389.
[17]. K. Jung, S. Lacroix. Robust interest points matching algorithm[C]. Proceedings of International Conference on Computer Vision. Canada, 2001: 538-543.
[18]. Q. Wu, Y. Yu. Matching and deformation for texture synthesis[C]. Proceedings of Proceeding of Computer Graphics. Los Angles, 2004: 362-365.
[19]. K. Gruman, T. Darrell. Efficient image matching with distributions of local invariant features[C]. Proceedings of Computer Vision and Pattern Recognition. Canada, 2005: 627-634.
[20]. Castleman K. R. Digital image processing[M]. Englewood Cliffs: Prentice-Hall International Inc, 1996.
[21]. A. Kaplan, E. Rivlin, I. Shimshoni. Robust feature matching across widely separated color images[C]. Proceedings of 17th International Conference on Pattern Recognition. 2004: 774-781.
[22]．全红艳，张田文，蔺想红．全景图拼接中的一种快速图像点对应策略[J]．机器人．2005，27(3)：236-240．
[23]．仵建宁，郭宝龙，冯宗哲．一种基于兴趣点特征匹配的图像镶嵌技术[J]．光电子·激光．2006，17(6)：733-737．
[24]. Siu A. M. K., Lau R. W. H. Image registration for image-based rendering[J]. IEEE Transaction on Image Processing. 2005, 14(2): 241-252.
[25]. Bouchafa S., Zavidovique B. Efficient cumulative matching for image registration[J]. Image and Vision Computing. 2006, 24: 70-79.
[26]. Jagannadan V., Prakash M. C., Sarma R. R., Rao G. V. P. Feature extraction and image registration of color images using fourier bases[C]. Proceedings of IEEE International Conference on Image Processing. 2005: 662-665.
[27]. Cordon O., Damas S., Santamaria J. Feature-based image registration by means of the chc evolutionary algorithm[J]. Image and Vision Computing. 2006, 24: 525-533.
[28]. Lazaridis G., Petrou M. Image registration using the waish transform[J]. IEEE Transaction on Image Processing. 2006, 15(8): 2343-2357.
[29]．封靖波，苏志勋，刘秀平．一种基于相似曲线的全景图自动拼接算法[J]．计算机学报．2003，26(11)：1604-1608．
[30]．沈沉，沈向洋，马颂德．基于图像的光照模型研究综述[J]．计算机学报．2000，23(12)：1261-1269．
[31]. Debevec P. Image based lighting[J]. IEEE Computer Graphics and Applications. 2002, 22(2): 26-34.
[32]. Agusanto K., Li L., Chuangui Z., Sing N.W. Photorealistic rendering for augmented reality using environment illumination[C]. Proceedings of The Second IEEE and ACM International Symposium on Mixed and Augmented Reality Singapore, 2003: 208-216.
[33]. Park Y., Choi T., Yoon K. Brdf recovering and scene re-lighting using an environment map[C]. Proceedings of International Conference on Computer Graphics, Imaging and Vision. China, 2005: 310-314.
[34]. Jensen T., Andersen M. S., Madsen C. B. Real-time image based lighting for outdoor augmented reality under dynamically changing illumination conditions[C]. Proceedings of International Conference on Computer Graphics Theory and Applications Portugal 2006: 323-330.
[35]. Nishino K., Sato Y., Ikeuchi K. Eigen-texture method: Appearance compression based on 3d model[C]. Proceedings of Proceedings of IEEE Conference on Computer Vision and Pattern Recognition 1999: 618-624.
[36]. Wong T.T., Heng P. A., Or S. h., Ng W. Y. Image-based rendering with controllable illumination[C]. Proceedings of Proceedings of the Eurographics Workshop on Rendering Techniques 1997: 13-22.
[37]. Shashua A., Riklin-Raviv T. The quotient image: Class-based re-rendering and recognition with varying illuminations[J]. IEEE Transaction on Pattern Analysis and Machine Intelligence. 2001, 25(2): 129-139.
[38]. Mukaigawa Y. Photometric image-based rendering for image generation in arbitrary illumination[C]. Proceedings of Proceedings of ICCV. 2001: 652-659.
[39]. Mukaigawa A. Photometric image-based rendering for virtual lighting image synthesis[C]. Proceedings of 2nd IEEE and ACM International Workshop on Augmented Reality. 1999: 115-124.
[40]. Jim B., NewellMartin. Texture and reflection in computer generated images[J]. Communications of ACM. 1976, 19(10): 542-547.
[41]. Yung-Yu C., E Z. D., J H. i. r. Environment matting extensions: Towards higher accuracy and realtime capture[C]. Proceedings of SIGGRAPH. New Orleans, 2000: 121-130.
[42]. Dan X., Pheng-ann H. Object image synthesis under changing of illumination[J]. Journal of Software. 2002, 13(4): 501-509.
[43]．姜忠鼎．基于深度全景视频的虚拟场景绘制技术研究[D]．浙江大学，2004
[44]. W.Freeman, T.Jones, E.Pasztor. Example-based super-resolution[J]. IEEE Computer Graphics and Applications. 2002, 22(2): 56-65.
[45]. X.Debevee, L.McMillan. Image-based modeling,rendering and lighting[J]. IEEE Computer Graphics and Applications. 2002, 22(2): 24-25.
[46]．孙庆杰，张晓鹏，吴恩华．一种基于bezier插值曲面的图像放大方法[J]．软件学报．1999，10(6)：570-574．
[47]．谢美华，王正明．图像分辨率增强的偏微分方程方法[J]．遥感学报．2005，9(6)：673-679．
[48]．朱宁，吴静，王忠谦．图像放大的偏微分方程方法[J]．计算机辅助设计与图形学学报．2005，17(9)：1941-1945．
[49]．逯峰．Tikhonov正则化方法在带噪数字图像缩放中的应用[J]．计算机辅助设计与图形学学报．2002，14(7)：651-654．
[50]．肖亮，吴慧中，韦志辉，汤淑春，刘扬．基于图的数字全变差模型及其带噪图像任意精度放大[J]．计算机辅助设计与图形学学报．2004，16(1)：51-57．
[51]．陶洪久，饶俊飞，周祖德．单幅图像的超分辨率重建方法[J]．武汉理工大学学报．2004，28(6)：943-946．
[52]. R.Y.Tsai, A.K.Huang. Multiframe image restoration and registration[C]. Proceedings of in Advances in Computer Vision and Registration. 1984: 317-339.
[53]. S B. Topics in multiframe superresolution restoration[D]. University of Notre Dame, 2004
[54]. Zomet A., Peleg S. Efficient super-resolution and applications to mosaics[C]. Proceedings of Proceeding of the Internation Conference on Pattern Recognition. 2000: 579-583.
[55]．谭兵，徐青，耿则勋，邢帅．超分辨率图象重建技术发展现状[J]．信息工程大学学报．2003，4(4)：93-97．
[56]. Tekalp A.M., KOzkan M., Sezan M.L. High resolution image reconstruction for lower-resolution image sequences and space-varying image restoration[C]. Proceedings of Proceedings of the IEEE International Conference on Acousics, Speech and Signal Proceeding on Acousics, Speech and Signal Processing. 1992: 169-172.
[57]. E. Kaltenbacher, R. C. Hadie. High resolution infrared image reconstruction using multiple low-resolution aliased frames[C]. Proceedings of IEEE International Conference on National Aerospace and Electronic. 1996: 702-709.
[58]. D.Keren, S.Peleg, R.Brada. Image sequence enhancement using subpixel displacements[C]. Proceedings of Proceedings of The IEEE Computer Vision and Pattern Recognition. 1998: 742-746.
[59]. M.Irani, S.peleg. Improving resolution by image registration[J]. CVGIP: Graphical Models and Image Processing 1991, 53(3): 231-239.
[60]. Tom B.C., A.K.Katsaggelos. Resolution enhancement of video sequences using motion compensation[C]. Proceedings of Proceedings of the IEEE International Conference on Image Processing. 1996: 713-716.
[61]. Messina G., Battiato S., Mancuso M., Buemi A. Improving image resolution by adaptive back-projection correction[J]. IEEE Transactions on Consumer Electronics. 2002, 48(3): 409-416.
[62]．黄华，樊鑫，齐春，朱世华．基于识别的凸集投影人脸图像超分辨率重建[J]．计算机研究与发展．2005，42(10)：1718-1725．
[63]. Ding J.Z., Jun B.H. A super-resolution method with ewa[J]. Journal of Computer Science and Technology. 2003, 18(6): 822-832.
[64]. R.Schultz R., L.Stevenson R. Improved difinition image expansion[C]. Proceedings of Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. 1992: 173-176.
[65]. Gunturk B.K., Gevrekci M. High-resolution image reconstruction from multiple differently exposed images[J]. IEEE Signal Processing Letters. 2006, 13(4): 197-200.
[66]. Farsiu S., Elad M., Milanfar P. Multiframe demosaicing and super-resolution of color images[J]. IEEE Transaction on Image Processing. 2006, 15(1): 141-160.
[67]. Farsiu S., Robinson D., Elad M., Milanfar P. Robust shift and add approach to super-resolution[C]. Proceedings of SPIE Conference on Applic Digital Signal and Image Process. 2003: 121-130.
[68]．韩玉兵，吴乐南．基于自适应滤波的视频序列超分辨率重建[J]．计算机学报．2006，29(4)：642-647．
[69]. Binghua S., Weiqi J. Pocs-mpmap based super-resolution image restoration[J]. ACTA PHOTONICA SINICA. 2003, 32(4): 502-504.
[70]. Yu Y., Sun Y.L., Liu H.-J. Steepest gradient descent method for estimation of motion parameters from image sequences[C]. Proceedings of International Symposium on Video Communication and Fiber Optic Networks. 1993: 297-301.
[71]. W.Freeman, E.Pasztor, O.Camichaei. Learning low-level vision[J]. International Journal of Computer Vision. 2000, 40(1): 25-47.
[72]. J.Sun, Zheng N., H.Tao, Shum H.Y. Image hallucination with primal sketch priors[C]. Proceedings of Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2003: 729-736.
[73]. E H., L T. Learning regularization functionals—a supervised training approach[J]. Inverse Problems. 2003, 19(3): 611-626.
[74]. Chappalli M.B., Bose N.K. Simultaneous noise filtering and super-resolution with second-generation wavelets[J]. IEEE Signal Processing Letters. 2005, 12(11): 772-775.
[75]. H K., KS H. Variational approaches to super-resolution with contrast enhancement and anisotropic diffusion[J]. Journal of Electronic Imaging. 2003, 12(1): 244-251.
[76]. S F., MD R. Fast and robust multiframe super resolution[J]. IEEE Transactions on Image Processing. 2004, 13(10): 1327-1344.
[77]. D B. Bilateral filtering and anisotropic diffusion: Towards a unified viewpoint[R]. Hewlett-Packard Laboratories Technical Report, 2000
[78]. M E. On the bilateral filter and ways to improve it[J]. IEEE Transactions on Image Processing. 2002, 11(10): 1141-1151.
[79]. V.M.Joshi, S.Chaudhuri, R.Panuganti. A learning-based method for image super-resolution from zoomed observations[J]. IEEE Transaction on Systems,man and cybernetics. 2005, 35(3): 527-537.
[80]. He H., Kondi L.P. An image super-resolution algorithm for different error levels per frame[J]. IEEE Transactions On Image Processing. 2006, 15(3): 592-603.
[81]. Robinson D., Milanfar P. Statistical performance analysis of super-resolution[J]. IEEE Transaction On Image Processing. 2006, 15(6): 1413-1428.
[82]．张新明，沈兰荪．基于多尺度边缘保持正则化的超分辨率复原[J]．软件学报．2003，14(6)：1075-1081．
[83]．孟庆武．预估计混叠度的map超分辨率处理算法[J]．软件学报．2004，15(2)：207-214．
[84]．邵文泽，韦志辉．基于广义huber-mrf图像先验模型的超分辨率复原算法[J]．软件学报．2006，17(12)．
[85]. R H., R G. Linear push broom-cameras[C]. Proceedings of International Proceedings of the 3rd European Conference on computer Vision. 1994: 555-566.
[86]. Jang K.H., Jung S.K. Constructing cylindrical panoramic image using equidistant matching[J], IEE Electronics Letters. 1999, 19(8): 1715-1716.
[87]．漆驰，刘强，孙家广．摄像机图像序列的全景图拼接[J]．计算机辅助设计与图形学学报．2001，13(7)：605-609．
[88]．周骥，石教英，赵友兵．图像特征点匹配的强壮算法[J]．计算机辅助设计与图形学学报．2002，14(8)：754-758．
[89]．岳焕印，郭华东，王长林．Sar干涉图的静态小波域map法滤波[J]．遥感学报．2002，6(6)：456-463．
[90]. J S.M. The discrete wavelet transform: Wedding the atrous and mallat algorithms[J]. IEEE Transactions on Signal Processing. 1992, 40(10): 2464-2482.
[91]．吴艳，李明，梁继民．一种图像融合新算法[J]．电子与信息学报．2003，25(1)：44-49．
[92]．章毓晋．图像工程[M]．清华大学出版社，1999．
[93]. Erik R., Michael A., Bruce G. Color transform between images[J]. IEEE Computer Graphics and Applications. 2001, 21(5): 34-41.
[94]．赵国英，向世明，李华．高阶矩在颜色传输中的应用[J]．计算机辅助设计与图形学学报．2004，16(1)：62-68．
[95]. Y.Tang Y., Liu J., Li B.F., Xi D. Multiresolution analysis in extraction of reference lines from documents with gray level background[J]. IEEE Transaction on Pattern Analysis and Machine Intelligence. 1997, 19(8): 921-926.
[96]. Tang Y.Y., Cheng H.D., Suen C.Y. Transformation-ring projection (trp) algorithm and its visi implementation[J]. Internation Pattern Recognition Artificial Intelligence. 1991, 5(12): 25-36.
[97]. L M., G B. Plenoptic modeling: An image-based rendering system[C]. Proceedings of Proceedings of the SIGGRAPH 1995: 39-46.
[98]．彭群生，鲍虎军，金小刚．计算机真实感图形的算法基础[M]．北京：科学出版社，1999．
[99]. Epstein R., Yuille A.L., Belhumeur P.N. Learning object representations from lighting variations.[C]. Proceedings of Proceedings of the Internelationai Workshop on Object Representation for Computer Vision. 1996:
[100]．张贤达．矩阵分析与应用[M]．北京：清华大学出版社，2004．
[101]. Http://cvc.Yale.Edu/people/faculty/belhumeur.Html. 2006.
[102]. Krim H., Hamza A.B. Towards a unified view of estimation: Variation vs. Statistica[C]. Proceedings of IEEE International conference on Image Processing. 2001: 577-580.
[103]. E. T J. On the rationale of maximum-entropy methods[C]. Proceedings of Proceedings of the IEEE. 1982: 939-952.
[104]. N S., R K., R M. A geometrical framework for low-level vision[J]. IEEE transaction on image processing. 1998, 7(3): 310-318.
[105]. P B., T. F C. Color tv: Total variation methods for restoration of vector-valued images[J]. IEEE Transaction On Image Processing. 1998, 7(3): 304-309.
[106]. Chan T. F., Osher S., Shen J. The digital tv filter and nonlinear denoising[J]. IEEE Transaction On Image Processing. 2001, 10(2): 231-241.
[107]．孙立峰，钟力，李云浩，胡晓峰．虚拟实景空间的实时漫游[J]．中国图象图形学报．1999，4(6)：507-513．
[108]．刘建威，崔杜武．基于全景图的多视点虚拟空间漫游技术[J]．计算机工程．2004，30(1)：153-154．
[109]. W. Klein A. An image-based framework for animated non-photorealistic rendering[D]. Princeton University, 2002
[110]. Curtis, S.Anderson, Seims J., Fleischer K., D.Salesin. Computer-generated watercolor[C]. Proceedings of Annual Conference Series, ACM SIGGRAPH. 1997: 421-430.
[111]. Hertzmann A., Jacobs C. E., Oliver N., Curless B., Salesin D. H. Image analogies[C]. Proceedings of SIGGRAPH 2001 Conference Proceedings. 2001: 327-340.
[112]. Cabral B., Leedom L. Imaging vector fields using line integral convolution[C]. Proceedings of Annual Conference Series, ACM SIGGRAPH. 1993: 263-272.
[113]. Wang C.-M., Lee J.-S. Using ILIC algorithm for an impressionist effect and stylized virtual environments[J]. Journal of Visual Languages and Computing. 2003, 14(3): 255-274.
[114]. Wang C.-M., Lee J.-S. Non-photorealistic rendering for aesthetic virtual environments[J]. Journal of Information Science and Engineering. 2004, 20(5): 923—948.
[115]. Kothe U. Edge and junction detection with an improved structure tensor[C]. Proceedings of DAGM Symposium Symposium for Pattern Recognition. 2003: 25-32.
[116]．程建刚，田捷，何余良，杨鑫．基于非线性扩散滤波的指纹增强算法[J]．自动化学报．2004，30(6)：854-862．
[117]. A. Aly H., Dubois E. Image up-sampling using total-variation regularization with a new observation model[J]. IEEE Transactions on Image Processing. 2005, 14(10): 1647-1659.
[118]. L R., S O., E F. Nonlinear total variation based noise removel algorithms[J]. Physical D: Nonlinear Phenomena. 1992, 10(60): 259-268.
[119]. Dasgupta S., Banerjee A. An augmented-reality-based real-time panoramic vision system for autonomous navigation[J]. IEEE Transaction on Systems,man and cybernetics. 2006, 36(1): 154-161.
[120]. Dasgupta S., Banerjee A. A real-time panoramic vision system for autonomous navigation[C]. Proceedings of Proceedings of the 36th conference on Winter simulation Washington, D. C, 2004: 1706-1712
[121]. Endo T., Katayama A., Tamura H., Hirose M., Tanikawa T., Saito M. Image-based walk-through system for large-scale scenes[C]. Proceedings of Proceeding of Virtual Systems and Multi-media. JIFU,Japan 1998: 269-274.
[122]. Bauermann I., Steinbach E. An image-based scene representation and rendering framework incorporating multiple representations[C]. Proceedings of Vision Modeling and Visualization Munich, Germany, 2003: 103-110.