面向遥感图像数据处理层应用的算法加速器体系结构研究
|
摘要
现代遥感技术正向着高光谱分辨率、高时间分辨率和高空间分辨率的方向迅猛发展,随之而来的是遥感数据量的急剧膨胀,从而给遥感图像处理带来了极大挑战。为满足实时处理海量遥感数据的需求,越来越多的图像处理算法正由传统的地面计算逐渐向星载计算转移,首当其冲的就是各种数据处理层算法。由于小波变换具有在时、频两域同时表征信号局部细节信息的能力和多分辨率分析特性,因此各种基于小波的算法成为遥感图像数据层处理的主流算法。此外,由于星载计算要求体积小、功耗低、计算能力强的处理方式,因此研究面向各种基于小波的遥感图像数据处理层算法的专用硬件加速器体系结构技术具有重要的前瞻性和巨大的应用价值。
本文首先从功能分类的角度对目前各种基于小波的主流遥感图像数据处理层算法进行概括总结,根据这些算法在计算特性和访存需求上的差异,将其大致归为三类:规则窗口访问型算法(含点访问型算法)、位访问型算法和不规则窗口访问型算法,并对各类算法的计算特点和访存需求进行了深入分析。
鉴于小波变换在本课题研究中的基础地位,特别针对二维小波提升变换算法提出了一种优化的2×2阵列结构。该阵列结构由两个行处理单元和两个列处理单元构成,行处理单元并行处理不同行上的变换操作,列处理单元则并行处理不同列上的变换操作,只要行处理单元积累了足够的列变换数据,即可启动列变换处理,从而充分利用算法中存在的不同行、不同列以及行、列变换之间的并行性,有效提高系统执行速度。并且2×2结构与二维小波提升算法的计算特性最为吻合,使得行处理单元所产生的数据能够及时被列处理单元消耗,从而有效减少了二者之间的中间缓存。实验结果表明:与现有最快结构相比,所提结构的处理速度提高了1.7倍,同时保持了适中的片上存储需求。
针对现代存储器不能有效支持规则窗口访问型算法所要求的窗口访问模式问题,提出了一种可扩展的多数据窗口并行结构,其中采用了由外部存储器、片上多体存储器和流水寄存器组构成的三级存储结构,不仅能够有效支持窗口访问模式,并且能够充分利用多个数据窗口之间的数据重用性。此外,本文还对所提结构的可扩展性进行了深入讨论,从并行计算所需带宽与可用存储带宽平衡的角度出发,实现了一种高效的多数据窗口并行结构。
本文以JPEG2000中的EBCOT算法为例,研究了位访问型算法的加速器结构。EBCOT算法由位平面编码和算术编码两个模块组成。本文首先针对传统位平面编码算法所导致的片上存储器访问模式失配问题,提出了一种基于子块的位平面编码存储优化调度方案,通过对编码块细分子块的方法巧妙避免了对片上编码块存储器的按位访问。基于子块的存储优化调度方案不仅能够有效解决访问模式失配问题,并且提供了一种新的子块并行方式。基于子块方案,本文实现了一个子块并行合并样本并行的位平面编码器结构。实验结果表明:与现有最快结构相比,所提结构的编码速度提高了30%以上,同时将片上存储容量需求减少了40%左右。进而本文还对几种可能的算术编码器结构进行分析和实现,提出了一种单符号编码三级流水结构。此外,还研究了位平面编码与算术编码之间的数据接口问题。
最后,本文以基于小波的遥感图像全局自动配准算法为例对不规则窗口访问型算法加速器结构进行了深入研究。不规则窗口访问型算法中数据窗口运动的不规则性导致数据预取范围过大、片上存储需求过高,不利于硬件高效实现,因此,本文提出了一种分块重采样算法,采用对结果图像进行分块重采样的策略有效缩小了数据预取范围,减少了片上存储需求。基于该分块算法,实现了一种高效的遥感图像全局自动配准算法加速器结构——BWAGIR结构,其中采用了流水化、并行重采样与相关系数计算、并行存储访问以及多BWAGIR模块并行策略,实验结果表明:采用5个BWAGIR模块能够达到与并行机上采用30个结点实现的并行算法相当的性能。
With the rapid development of remote sensing technology towards high spatialresolution, high frequency resolution and high spectral resolution, the size of remotesensing images grows significantly. To meet the requirement of processing massiveremote sensing data realtimely, more and more image processing algorithms are be-ing migrated from ground computing to on-board computing. The wavelet-basedremote image processing algorithms have become most popular because of the ex-cellent features of wavelet transform. And on-board computing requires low powerconsumption, small size and powerful computational capabilities. Therefore, it’s ofgreat significance to study on specific accelerators for kinds of remote sensing imageprocessing algorithms.
Firstly, a comprehensive investigation is developed on popular remote sensingimage processing algorithms. And based on the di?erent features of memory ac-cess, they are classified into three categories - regular-window-access algorithms,bit-access algorithms and irregular-window-access algorithms. For each category,the computation and memory requirements are analyzed.
An optimized 2×2 array archtecture for lifting-based two dimension discretewavelet transform (2D-DWT) is proposed specifically because wavelet transformis the foundation of this research. The proposal is composed of two row proces-sor (RP) which operate on di?erent rows and two column processor (CP) whichoperate on di?erent columns. It exploits the parallelisms between di?erent rowtransforms, between di?erent column transforms, and between row transform andcolumn transform to improve the excution speed. Because the array can coincidewith the computation of 2D-DWT, the data produced by RPs can be consumed byCPs in time. The bu?er between RPs and CPs are also reduced.
For the algorithms with regular-window-access type, a scalable multi-data-windows-parallel architecture is proposed in which a three-level memory hierarchyis employed. Compared with other related works, our architecture can not onlysupport the window access pattern to memory, but also support the data reusabilitybetween multiple data windows. And also the scalability of proposed architecture isdicussed based on the balance of the computing requirements and available memory bandwidth.
As a typical bit-access algorithm, the embedded block coding with optimaltruncation (EBCOT) algorithm in JPEG2000 standard is composed of bit-planeencoder (BPE) and arithmetic encoder (AE). A subblock-based BPE scheme is pro-posed firstly to conquer the mismatch in memory access caused by the traditionalscheme. And also, the new scheme makes it possible to encode multiple subblockparallelly. Based on the subblock-based scheme, a subblock-based BPE architectureis proposed in which subblock-parallel and sample-parallel policies are employed.The architecture not only improves the encoding speed, but also reduces the re-quirements for on-chip memory. For the AE part, a single-symbol coding three-stagepipeline architecture is proposed after an investigation of several popular pipelines.And also the interface between BPE and AE is discussed to balance the di?erencein speed.
Finally, the wavelet-based automated global remote sensing image registration(WAGIR) is taken as an example to study the accelerator architecture for irregular-window-access algorithm. The resampling process and the computation of corre-lation coe?cient are the kernels of WAGIR. So the WAGIR is accelerated by ac-celerating the resampling and computation of correlation coe?cient. Traditionalresampling process demands great on-chip memories, and cannot be implemented inhardware e?ciently because of the irregular sliding of data window in the referenceimage. A block resampling algorithm is proposed to address this problem. In thenew algorithm, the result image is resampled block by block to reduce the scopeof required data. Based on this scheme, a BWAGIR (block WAGIR) architectureis proposed in which pipelining, parallel resampling and computation of correla-tion coe?cient, parallel memory access, and parallel multiple BWAGIR modulesare employed.
本文首先从功能分类的角度对目前各种基于小波的主流遥感图像数据处理层算法进行概括总结,根据这些算法在计算特性和访存需求上的差异,将其大致归为三类:规则窗口访问型算法(含点访问型算法)、位访问型算法和不规则窗口访问型算法,并对各类算法的计算特点和访存需求进行了深入分析。
鉴于小波变换在本课题研究中的基础地位,特别针对二维小波提升变换算法提出了一种优化的2×2阵列结构。该阵列结构由两个行处理单元和两个列处理单元构成,行处理单元并行处理不同行上的变换操作,列处理单元则并行处理不同列上的变换操作,只要行处理单元积累了足够的列变换数据,即可启动列变换处理,从而充分利用算法中存在的不同行、不同列以及行、列变换之间的并行性,有效提高系统执行速度。并且2×2结构与二维小波提升算法的计算特性最为吻合,使得行处理单元所产生的数据能够及时被列处理单元消耗,从而有效减少了二者之间的中间缓存。实验结果表明:与现有最快结构相比,所提结构的处理速度提高了1.7倍,同时保持了适中的片上存储需求。
针对现代存储器不能有效支持规则窗口访问型算法所要求的窗口访问模式问题,提出了一种可扩展的多数据窗口并行结构,其中采用了由外部存储器、片上多体存储器和流水寄存器组构成的三级存储结构,不仅能够有效支持窗口访问模式,并且能够充分利用多个数据窗口之间的数据重用性。此外,本文还对所提结构的可扩展性进行了深入讨论,从并行计算所需带宽与可用存储带宽平衡的角度出发,实现了一种高效的多数据窗口并行结构。
本文以JPEG2000中的EBCOT算法为例,研究了位访问型算法的加速器结构。EBCOT算法由位平面编码和算术编码两个模块组成。本文首先针对传统位平面编码算法所导致的片上存储器访问模式失配问题,提出了一种基于子块的位平面编码存储优化调度方案,通过对编码块细分子块的方法巧妙避免了对片上编码块存储器的按位访问。基于子块的存储优化调度方案不仅能够有效解决访问模式失配问题,并且提供了一种新的子块并行方式。基于子块方案,本文实现了一个子块并行合并样本并行的位平面编码器结构。实验结果表明:与现有最快结构相比,所提结构的编码速度提高了30%以上,同时将片上存储容量需求减少了40%左右。进而本文还对几种可能的算术编码器结构进行分析和实现,提出了一种单符号编码三级流水结构。此外,还研究了位平面编码与算术编码之间的数据接口问题。
最后,本文以基于小波的遥感图像全局自动配准算法为例对不规则窗口访问型算法加速器结构进行了深入研究。不规则窗口访问型算法中数据窗口运动的不规则性导致数据预取范围过大、片上存储需求过高,不利于硬件高效实现,因此,本文提出了一种分块重采样算法,采用对结果图像进行分块重采样的策略有效缩小了数据预取范围,减少了片上存储需求。基于该分块算法,实现了一种高效的遥感图像全局自动配准算法加速器结构——BWAGIR结构,其中采用了流水化、并行重采样与相关系数计算、并行存储访问以及多BWAGIR模块并行策略,实验结果表明:采用5个BWAGIR模块能够达到与并行机上采用30个结点实现的并行算法相当的性能。
With the rapid development of remote sensing technology towards high spatialresolution, high frequency resolution and high spectral resolution, the size of remotesensing images grows significantly. To meet the requirement of processing massiveremote sensing data realtimely, more and more image processing algorithms are be-ing migrated from ground computing to on-board computing. The wavelet-basedremote image processing algorithms have become most popular because of the ex-cellent features of wavelet transform. And on-board computing requires low powerconsumption, small size and powerful computational capabilities. Therefore, it’s ofgreat significance to study on specific accelerators for kinds of remote sensing imageprocessing algorithms.
Firstly, a comprehensive investigation is developed on popular remote sensingimage processing algorithms. And based on the di?erent features of memory ac-cess, they are classified into three categories - regular-window-access algorithms,bit-access algorithms and irregular-window-access algorithms. For each category,the computation and memory requirements are analyzed.
An optimized 2×2 array archtecture for lifting-based two dimension discretewavelet transform (2D-DWT) is proposed specifically because wavelet transformis the foundation of this research. The proposal is composed of two row proces-sor (RP) which operate on di?erent rows and two column processor (CP) whichoperate on di?erent columns. It exploits the parallelisms between di?erent rowtransforms, between di?erent column transforms, and between row transform andcolumn transform to improve the excution speed. Because the array can coincidewith the computation of 2D-DWT, the data produced by RPs can be consumed byCPs in time. The bu?er between RPs and CPs are also reduced.
For the algorithms with regular-window-access type, a scalable multi-data-windows-parallel architecture is proposed in which a three-level memory hierarchyis employed. Compared with other related works, our architecture can not onlysupport the window access pattern to memory, but also support the data reusabilitybetween multiple data windows. And also the scalability of proposed architecture isdicussed based on the balance of the computing requirements and available memory bandwidth.
As a typical bit-access algorithm, the embedded block coding with optimaltruncation (EBCOT) algorithm in JPEG2000 standard is composed of bit-planeencoder (BPE) and arithmetic encoder (AE). A subblock-based BPE scheme is pro-posed firstly to conquer the mismatch in memory access caused by the traditionalscheme. And also, the new scheme makes it possible to encode multiple subblockparallelly. Based on the subblock-based scheme, a subblock-based BPE architectureis proposed in which subblock-parallel and sample-parallel policies are employed.The architecture not only improves the encoding speed, but also reduces the re-quirements for on-chip memory. For the AE part, a single-symbol coding three-stagepipeline architecture is proposed after an investigation of several popular pipelines.And also the interface between BPE and AE is discussed to balance the di?erencein speed.
Finally, the wavelet-based automated global remote sensing image registration(WAGIR) is taken as an example to study the accelerator architecture for irregular-window-access algorithm. The resampling process and the computation of corre-lation coe?cient are the kernels of WAGIR. So the WAGIR is accelerated by ac-celerating the resampling and computation of correlation coe?cient. Traditionalresampling process demands great on-chip memories, and cannot be implemented inhardware e?ciently because of the irregular sliding of data window in the referenceimage. A block resampling algorithm is proposed to address this problem. In thenew algorithm, the result image is resampled block by block to reduce the scopeof required data. Based on this scheme, a BWAGIR (block WAGIR) architectureis proposed in which pipelining, parallel resampling and computation of correla-tion coe?cient, parallel memory access, and parallel multiple BWAGIR modulesare employed.
引文
[1]朱述龙,张占睦.遥感图像获取与分析.北京:科学出版社, 2000
[2]王耀南.卫星遥感图像处理与分析系统的新方法研究及其应用[博士后研究工作报告].长沙:国防科学技术大学, 1997
[3]周海芳.遥感图像并行处理算法的研究与应用[博士学位论文].长沙:国防科学技术大学, 2003
[4] R. C. Gonzalez, R. E. Woods.阮秋琦,阮宇智等译.数字图像处理(第二版).北京:电子工业出版社, 2007
[5] http://www.ermapper.com/
[6] http://www.pcigeomatics.com/products/products overview.html
[7] http://www.erdas.com/Products/ERDASProductInformation/tabid/84/currentid/1050/default.aspx
[8] http://www.ittvis.com/ProductServices/ENVI.aspx
[9] http://159.226.117.45/rsim/index.htm
[10] http://www.intergraph.com/global/cn/
[11]赵瑞珍.小波理论及其在图像、信号处理中的算法研究[博士学位论文].西安:西安电子科技大学, 2001
[12]费双波.小波去噪算法研究及小波硬件实现[硕士学位论文].北京:北京交通大学, 2006
[13] W. Sweldens. The Lifting Scheme: A Custom-Design Construction of BiorthogonalWavelets. Applied and Computational Harmonic Analysis. 1996, 3(2):186–200
[14] W. Sweldens. The Lifting Scheme: A Construction of Second Generation Wavelets.SIAM Journal of Mathematical Analysis. 1998, 29(2):511–546
[15] S. G. Mallat. A Theory for Multiresolution Signal Decomposition: The WaveletRepresentation. IEEE Transactions on Pattern Analysis and Machine Intelligence.1989, 11(7):674–693
[16] W. B. Pennebaker, J. L. Mitchell. JPEG: Still Image Data Compression Standard.NY: Van Nostrand Reinhold, 1992
[17] M. Boliek, C. Christopoulos, E. Majani. JPEG2000 Part I Final Committee DraftVersion 1.0. ISO/IEC JTC1/SC29 WG1-2000, 2000
[18] David S. Taubman, Michael W. Marcellin.魏江力,柏正尧等译. JPEG2000图像压缩基础、标准和实践.北京:电子工业出版社, 2004
[19]小野定康,铃木纯司.强增福译. JPEG2000技术.北京:科学出版社, 2004
[20] http://www.altera.com.cn/
[21] http://china.xilinx.com/index.shtml
[22]李弼程,彭天强,彭波等.智能图像处理技术.北京:电子工业出版社, 2004
[23] M. Srikar, S. Srinivasan. A Novel Architecture for Lifting-Based DiscreteWaveletTransform for JPEG2000 Standard suitable for VLSI implementation. Proceedingsof the 16th International Conference on VLSI Design (VLSI’03). 2003, 202–207
[24] Chung-Jr Lian, Kuan-Fu Chen, HongHui Chen, Liang-Gee Chen. Analysis and Ar-chitecture Design of Lifting Based DWT and EBCOT for JPEG2000. Proceedings ofthe 2001 International Symposium on VLSI Technology, Systems, and Applications(VLSI-TSA’01). 2001, 180–183
[25] Jen-Shiun Chiang, Chih-Hsien Hsia, Hsin-Jung Chen, Te-Jung Lo. VLSI Architec-ture of Low Memory and High Speed 2D Lifting-Based Discrete Wavelet Transformfor JPEG2000 Applications. Proceedings of the 2005 IEEE International Symposiumon Circuits and Systems (ISCAS’05). 2005, vol. 5, 4554–4557
[26] S. Barua, J.E. Carletta, K.A. Kotteri, A.E. Bell. An E?cient Architecture forLifting-based Two-dimensional Discrete Wavelet Transforms. Integration, the VLSIJournal. 2005, 38(3):341–352
[27] K. Andra, C. Chakrabarti, T. Acharya. A VLSI Architecture for Lifting-basedForward and Inverse Wavelet Transform. IEEE Transactions on Signal Processing.2002, 50(4):966–977
[28] K. Andra, C. Chakrabarti, T. Acharya. A VLSI architecture for lifting-based wavelettransform. Proceedings of the 2000 IEEE Workshop on Signal Processing Systems(SIPs’00). 2000, 70–79
[29]董文辉,刘明业. JPEG2000小波提升算法的硬件设计.电子学报. 2003,31(11):1674–1677
[30] G. Kuzmanov, G. Gaydadjiev, S. Vassiliadis. Multimedia Rectangularly AddressableMemory. IEEE Transactions on Multimedia. 2006, 8(2):315–322
[31] J. W. Park. An E?cient Bu?er Memory System for Subarray Access. IEEE Trans-actions on Parallel and Distributed Systems. 2001, 12(3):316–335
[32] G. Kuzmanov, S. Vassiliadis, J. Van Eijndhoven. A 2D Addressing Mode for Mul-timedia Applications. Proceedings of 2001 International Symposium on Systems,Architectures, Modeling, and Simulation (SAMOS’01). 2001, vol. 2268, 291–306
[33] M. B. Haverkamp, G. Kuzmanov, S. Vassiliadis. Implementing 2D Memory Bu?ersfor MPEG. Proceedings of PRORISC’03). 2003, 90–94
[34] Chung-Jr Lian, Kuan-Fu Chen, Hong-Hui Chen, Liang-Gee Chen. Analysis and Ar-chitecture Design of Block-coding Engine for EBCOT in JPEG2000. IEEE Trans-actions on Circuits and Systems for Video Technology. 2003, 13(3):219–230
[35]杨珂,刘明业. JPEG2000系数位平面编码的FPGA设计.计算机应用研究.2005, (10):157–161
[36] K. Andra, C. Chakrabarti, T. Acharya. A High-Performance JPEG2000 Archi-tecture. IEEE Transactions on Circuits and Systems for Video Technology. 2003,13(3):209–218
[37] A. K. Gupta, D. Taubman, S. Nooshabadi. High-speed VLSI Architecture for BitPlane Encoder of JPEG2000. Proceedings of the 47th IEEE International MidwestSymposium on Circuits and Systems (MWSCAS’04). 2004, vol. 2, 341–352
[38] A. K. Gupta, S. Nooshabadi, D. Taubman. Optimal 2 Sub-bank Memory Architec-ture for Bit Plane Coder of JPEG2000. Proceedings of the 2005 IEEE InternationalSymposium on Circuits and Systems (ISCAS’05). 2005, vol. 5, 4373–4376
[39]赵兴,沈海斌,阳晔.一种新型的样本并行JPEG2000编码器EBCOT体系.视频技术应用与工程. 2005, (4):99–102
[40] Zhao Xing, Yang Ye, Qin Xing, Wu Tuo, Shen Hai-Bin. A Cycle-E?cient Sample-Parallel EBCOT Architecture for JPEG2000 Encoder. Proceedings of the 2004International Symposium on Intelligent Multimedia, Video and Speech Processing(ISIMP’04). 2004, 386–389
[41] Jen-Shiun Chiang, Yu-Sen Lin, Chang-Yo Hsieh. E?cient Pass-Parallel Architecturefor EBCOT in JPEG2000. Proceedings of the 2002 IEEE International Symposiumon Circuits and Systems (ISCAS’02). 2002, vol. 1, I–773–I–776
[42] Jen-Shiun Chiang, Chun-Hau Chang, Yu-Sen Lin, Chang-Yuo Hsieh. High Through-put Rate EBCOT Architecture for JPEG2000. Proceedings of the 46th IEEE Inter-national Midwest Symposium on Circuits and Systems (MWSCAS’03). 2003, vol. 2,610–613
[43] Jen-Shiun Chiang, Chun-Hau Chang, Yu-Sen Lin, Chang-You Hsieh, Chih-HsiehHsia. High-speed EBCOT with Dual Context-modeling Coding Architecture forJPEG2000. Proceedings of the the 2004 International Symposium on Circuits andSystems (ISCAS’04). 2004, vol. 3, 865–868
[44]刘凯,李云松,吴成柯.高性能的EBCOT编码及其VLSI结构.软件学报. 2006,17(7):1553–1560
[45]刘凯,吴成柯,李云松,庄怀宇.比特平面并行的EBCOT编码及其VLSI结构.计算机学报. 2004, 27(7):928–935
[46] Liu Kai, Wu Chengke, Li Yunsong. A High-Performance VLSI Architecture ofEBCOT Block Coding in JPEG2000. Journal of Electronics. 2006, 23(1):89–93
[47] H.-C. Fang, T.-C. Wang, C.-J. Lian, T.-H. Chang, L.-G. Chen. Parallel EmbeddedBlock Coding Architecture for JPEG 2000. IEEE Transactions on Circuits andSystems for Video Technology. 2005, 15(9):1086–1097
[48] L.-F. Chen, T.-L. Huang, T.-M. Chou, Y.-K. Lai. Analysis and Architecture Designfor Memory E?cient Parallel Embedded Block Coding in JPEG2000. Proceedings ofthe 2006 IEEE International Conference on Acoustics, Speech, and Signal Processing(ICASSP’06). 2006, vol. 3, III–964–III–967
[49] Y. Li, R. E. Aly, M. A. Bayoumi, S. A. Mashali. Parallel Highspeed Architecturefor EBCOT in JPEG2000. Proceedings of the 2003 IEEE International Conferenceon Acoustics, Speech, and Signal Processing (ICASSP’03). 2003, vol. 2, 481–484
[50] Y. Li, M. Bayoumi. Three-level Parallel High Speed Architecture for EBCOT inJPEG2000. Proceedings of the 2005 IEEE International Conference on Acoustics,Speech, and Signal Processing (ICASSP’05). 2005, vol. 5, 5–8
[51] Y. Li, M. Bayoumi. A Three-level Parallel High-speed Low-power Architecturefor EBCOT of JPEG2000. IEEE Transactions on Circuits and Systems for VideoTechnology. 2006, 16(9):1153–1163
[52] H.-C. Fang, Y.-W. Chang, C.-C. Cheng, C.-C. Chen, L.-G. Chen. Memory E?cientJPEG 2000 Architecture with Stripe Pipeline Scheme. Proceedings of the 2005 IEEEInternational Conference on Acoustics, Speech, and Signal Processing (ICASSP’05).2005, vol. 5, 1–4
[53]韩彦菊,许超. JPEG2000分数位平面编码器的FPGA电路实现.计算机工程.2005, 31(15):183–185
[54]许超,韩彦菊.一种快速的分数位平面编码方法及其电路结构.计算机学报.2005, 28(10):1650–1656
[55] Bao-Feng Li, Yong Dou. Subblock-based BPE Scheme to Conquer Mismatch inMemory Access Pattern. Proceedings of the 2008 IEEE International Conferenceon Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP’08).2008, 814–817
[56] C. R. Castro-Pareja, R. Shekhar. Hardware Acceleration of Mutual Information-Based 3D Image Registration. Journal of Imaging Science and Technology. 2005,49(2):105–113
[57] C.R. Castro-Pareja, J. M. Jagadeesh, R. Shekhar. FAIR: A Hardware Architecturefor Real-Time 3D Image Registration. IEEE Transactions on Information Technol-ogy in Biomedicine. 2003, 7(4):426–434
[58]肖莎,尹酉,刘江沙.基于小波变换的图像增强技术.科技咨询导报. 2007,2:45–46
[59]王欣,王云霄,于晓,庞云阶.基于多小波阈值收缩与子带增强的图像去噪.哈尔滨工业大学学报. 2008, 40(1):152–154
[60] T. J. Brown. An Adaptive Strategy for Wavelet Based Image Enhancement. Pro-ceedings of 2000 Irish Machine Vision and Image Processing Conference. 2000, 67–81
[61]王惠,王宇,蔡光程.基于小波阈值的图像增强算法.电脑知识与技术. 2007,23:1405–1408
[62]陈志坚,成培,李峰.基于小波变换的多层次图像增强算法.长沙理工大学学报. 2006, 3(2):69–72
[63] R. J. Claudio, S. Jacob. Adaptive Image Denosing and Edge Enhancement in Scale-space Using the Wavelet Transform. Pattern Recognition Letters. 2003, 24(7):965–971
[64]刘劲,康志伟,何怡刚.基于小波变换的反锐化掩模图像增强新算法.计算机工程. 2007, 33(21):179–181
[65] J. G. Leu. Edge Sharpening Through Ramp Width Reduction. Image and VisionComputing. 2000, 18(6):510–514
[66]徐凌,刘薇,杨光.结合小波域变换和空间域变换的图像增强方法.波谱学杂志. 2007, 24(4):462–468
[67]陈帅.基于小波分析的遥感图像增强研究[硕士学位论文].成都:成都理工大学, 2006
[68]高飞,杨平先,孙兴波.基于小波变换与阀值收缩法的图像增强去噪.四川理工学院学报. 2006, 19(2):8–11
[69]赵同阳,张晓帆,周可法,徐仕琪.小波变换在遥感图像降噪中的应用.中国科技信息. 2007, 1:248–250
[70]武文波,张迅,陈静.小波阈值的改进及在遥感图像去噪中的应用.遥感技术与应用. 2007, 22(6):722–726
[71]赵继印,郝志成,李建坡.小波自适应比例改进算法在图像去噪中的应用.光电工程. 2006, 33(1):81–84
[72] M. K. Mihcak, I. Kozintsev, K. Ramchandran, P. Moulin. Low-Complexity ImageDenoising Based on Statistical Modeling of Wavelet Coe?cients. IEEE SIGNALPROCESSING LETTERS. 1999, 6(12):300–303
[73] M. K. Mihcak, I. Kozintsev, K. Ramchandran. Spatially Adaptive Statistical Mod-eling of Wavelet Image Coe?cients and Its Application to Denoising. Proceed-ings of 1999 International Conference on Acoustics,Speech and Signal Processing(ICASSP’99). 1999, 3253–3256
[74] J. M. Shapiro. Embedded Image Coding Using Zerotrees of Wavelet Coe?cients.IEEE Transactions on Signal Processing. 1993, 41(12):3445–3462
[75] A. Said, W. A. Pearlman. A New, Fast, and E?cient Image Codec Based on SetPartitioning in Hierarchical Trees. IEEE Transactions on Circuits and Systems forVideo Technology. 1996, 6(3):243–250
[76] Krishnaraj M. Varma. Fast Split Arithmetic Encoder Architectures and PerceptualCoding Methods for Enhanced JPEG2000 Performance [Ph.D Thesis]. Blacksburg:Virginia Polytechnic Institute and State University, 2006
[77] D. Taubman. High Performance Scalable Image Compression with EBCOT. IEEETransactions on Image Processing. 2000, 9(7):1158–1170
[78] L. G. Brown. A Survey of Image Registration Techniques. ACM Computing Surveys.1992, 24(4):325–376
[79] B. Zitova, J. Flusser. Image Registration Methods: A Survey. Image and VisionComputing. 2003, 21(11):977–1000
[80] J. P. Djamdji, A. Bijaoui, R. Maniere. Geometrical Registration of Images: The Mul-tiresolution Approach. Photogrammetric Engineering and Remote Sensing. 1993,59(5):645–653
[81] J. Flusser. An Adaptive Method for Image Registration. Pattern Recognition. 1992,25(1):45–54
[82] B. S. Manjunath, C. Shekhar, R. Chellappa. A New Approach to Image FeatureDetection with Applications. Pattern Recognition. 1996, 29(4):627–640
[83] A.D. Ventura, A. Rampini, R. Schettini. Image Registration by The Recognition ofCorresponding Structures. IEEE Transactions on Geoscience and Remote Sensing.1990, 28(5):305–387
[84] Q. Zheng, R. Chellappa. A Computational Vision Approach to Image Registration.IEEE Transactions on Image Processing. 1993, 2(7):311–326
[85] H. Li, B. S. Manjunath, S. K. Mitra. A Contour-based Approach to MultisensorImage Registration. IEEE Transactions on Image Processing. 1995, 4(3):320–334
[86] J. P. Djamdji, A. Bijaoui. Disparity Analysis: A Wavelet Transform Approach.IEEE Transactions on Geoscience and Remote Sensing. 1995, 33(1):67–76
[87] H. H. Li, Y. T. Zhou. Wavelet-based Point Feature Extractor for Multisensor Im-age Registration. Proceedings of SPIE Aerosense Wavelet Applications III. 1996,2762:524–534
[88] Y.C. Hsieh, D.M. McKeown. Performance Evaluation of Scene Registration andStereo Matching for Cartographic Feature Extraction. IEEE Transactions on Pat-tern Analysis and Machine Intelligence. 1992, 14(2):214–238
[89] J. L. Moigne, W. J. Campbell, R. F. Cromp. An Automated Image RegistrationTechnique Based on The Correlation of Wavelet Features. IEEE Transactions onGeoscience and Remote Sensing. 2002, 40(8):1849–1864
[90] J. L. Moigne, A. Cole-Rhodes, R. Eastman. Multi-sensor Registration of EarthRemotely Sensed Imagery. Proceedings of the 8th SPIE International Symposiumon Remote Sensing. 2001, vol. 4541, 1–10
[91] J. L. Moigne, I. Zavorin. Use of Wavelets for Image Registration. Proceedings ofthe SPIE Aerosense 2000, Wavelet Applications VII. 2000, vol. 4056, 99–108
[92] P. Thevenaz, U. E. Ruttimann, M. Unser. A Pyramid Approach to Sub-pixel Regis-tration Based on Intensity. IEEE Transactions on Image Processing. 1998, 7(1):27–41
[93] R. L. Allen, F. A. Kamangar, E.M. Stokely. Laplacian and Orthogonal WaveletPyramid Decompositions in Coarse-to-fine Registration. IEEE Transactions on Sig-nal Processing. 1993, 41(12):3536–3541
[94] Q. S. Chen, M. Defrise, F. Deconinck. Symmetric Phase-Only Matched Filtering ofFourier-Mellin Transforms for Image Registration and Recognition. IEEE Transac-tions on Pattern Analysis and Machine Intelligence. 1994, 16(12):1156–1168
[95] J. C. Olivo, J. Deubler, C. Boulin. Automatic Registration of Images by a Wavelet-based Multiresolution Approach. Proceedings of SPIE Wavelet Applications in Sig-nal and Image Processing III. 1995, vol. 2569, 234–244
[96] H. Shekarforoush, M. Berthod, J. Zerubia. Subpixel Image Registration by Estimat-ing the Polyphase Decomposition of the Crosspower Spectrum. Proceedings of 1996IEEE Conference on Computer Vision and Pattern Recognition. 1996, 532–537
[97] R. C. Hardie, K. J. Barnard, E. E. Armstrong. Joint MAP Registration and High-Resolution Image Estimation Using a Sequence of Undersampled Images. IEEETransactions on Image Processing. 1997, 6(12):1621–1633
[98] R. J. Althof, M.G.J. Wind, J.T. Dobbins. A Rapid and Automatic Image Regis-tration Algorithm with Subpixel Accuracy. IEEE Transactions on Medical Imagine.1997, 16(6):308–316
[99] H. F. Zhou, X. J. Yang, H. Z. Liu. First Evaluation of Parallel Methods of AutomaticGlobal Image Registration Based on Wavelets. Proceedings of the 2005 InternationalConference on Parallel Processing (ICPP’05). 2005, 129–136
[100] H. F. Zhou, Y. Tang, X. Yang. Research on Grid-Enabled Parallel Strategies ofAutomatic Wavelet-based Registration of Remote-Sensing Images and Its Applica-tion in ChinaGrid. Proceedings of the 4th International Conference on Image andGraphics (ICIG’07). 2007, 725–730
[101]敬忠良,肖刚,李振华.图像融合:理论与应用.北京:高等教育出版社, 2007
[102] D. A. Yocky. Image Merging and Data Fusion by Means of the Discrete Two-Dimensional Wavelet Transform. Journal of the Optical Society of America A:Optics, Image Science, and Vision. 1995, 12(9):1834–1841
[103] J. Nunez, X. Otazu, O. Fors, A. Prades, V. Pala, R. Arbiol. Multiresolution-basedImage Fusion with Additive Wavelet Decomposition. IEEE Transactions on Geo-science and Remote Sensing. 1999, 37(3):1204–1211
[104]陶观群,李大鹏,陆光华.基于小波变换的不同融合规则的图像融合研究.红外与激光工程. 2003, 32(2):173–176
[105]陶冰洁,王敬儒.采用小波分析的图像融合方法评述.计算机工程与应用.2005, 41(25):16–19
[106]强赞霞,彭嘉雄,王洪群.基于小波变换局部方差的遥感图像融合.华中科技大学学报. 2003, 31(6):89–91
[107]王国峰,张科,李言俊.基于小波多分辨分析的图像融合方法.红外与激光工程. 2003, 32(5):513–515
[108]霍宏涛,游先祥.小波变换在遥感图像融合中的应用研究.中国图像图形学报. 2003, 8A(5):551–556
[109] Ke Zhu, Xiao-Fang Zhou, Lin Hua, Qian-Ling Zhang. An E?cient VLSI Implemen-tation of DWT for JPEG2000. Proceedings of the 2003 International Conference onNeural Networks and Signal Processing (ICNNSP’03). 2003, vol. 2, 990–993
[110] http://www.openjpeg.org/
[111] C. Christopoulos, A. Skodras, T. Evrahimi. The JPEG2000 Still Image Coding Sys-tem: An Overview. IEEE Transactions on Consumer Electronics. 2000, 46(4):1103–1127
[112] K.C.B. Tan, T. Arslan. An Embedded Extension Algorithm for the Lifting BasedDiscrete Wavelet Transform in JPEG2000. Proceedings of the 2002 IEEE Interna-tional Conference on Acoustics, Speech, and Signal Processing (ICASSP’02). 2002,vol. 4, IV–3513–IV–3516
[113] K.C.B. Tan, T. Arslan. Low power embedded extension algorithm for lifting-baseddiscretewavelet transform in JPEG2000. Electronics Letters. 2001, 37(22):1328–1330
[114] C. Chrysafis, A. Ortega. Line-based, Reduced Memory, Wavelet Image Compression.IEEE Transactions on Image Processing. 2000, 9(3):378–389
[115]兰旭光,郑南宁,梅魁志,刘跃虎. JPEG2000并行阵列式小波滤波器的VLSI结构设计.电子学报. 2004, 32(11):1806–1809
[116] G. Pastuszak. A Novel Architecture of Arithmetic Coder in JPEG2000 Based onParallel Symbol Encoding. Proceedings of the 2004 IEEE International Conferenceon Parallel Computing in Electrical Engineering (PARELEC’04). 2004, 303–308
[117] Jen-Shiun Chiang, Chang-Yo Hsieh, Jin-Chan Liu, Cheng-Chih Chien. ConcurrentBit-Plane Coding Architecture for EBCOT in JPEG2000. Proceedings of the 2006International Symposium on Circuits and Systems (ISCAS’06). 2006, vol. 5, 4595–4598
[118]王勇,郑南宁,梅魁志,朱悦心,刘跃虎.一种高效的JPEG2000位平面编码器设计.西安交通大学学报. 2005, 39(2):158–161
[119] Y.-Z. Zhang, C. Xu, L.-B. Chen. A Dual-Symbol Coding Arithmetic Coder Archi-tecture Design for High Speed EBCOT Coding Engine in JPEG2000. Proceedingsof the 2005 International Conference on ASIC (ASICON’05). 2005, vol. 1, 261–264
[120] M. Dyer, D. Taubman, S. Nooshabadi, A. K. Gupta. Concurrency Techniques forArithmetic Coding in JPEG2000. IEEE Transactions on Circuits and Systems I:Regular Papers. 2006, 53(6):1203–1213
[121] Yu-Wei Chang, Hung-Chi Fang, Liang-Gee Chen. High Performance Two-SymbolArithmetic Encoder in JPEG2000. Proceedings of the 2004 IEEE InternationalSymposium on Consumer Electronics (ISCE’04). 2004, 101–104
[122] N. Noikaew, O. Chitsobhuk. Dual Symbol Processing for MQ Arithmetic Coder inJPEG2000. Proceedings of the 2008 IEEE Congress on Image and Signal Processing(CISP’08). 2008, vol. 1, 521–524
[123] Y. Li, M. Elgamel, M. Bayoumi. A Partial Parallel Algorithm and Architecturefor Arithmetic Encoder in JPEG2000. Proceedings of the 2005 IEEE InternationalSymposium on Circuits and Systems (ISCAS’05). 2005, vol. 5, 5198–5201
[124] G. Wolberg, S. Zokai. Image Registration for Perspective Deformation Recovery.Proceedings of the SPIE Conference on Autonatic Target Recognition X. 2000, 1–12
[125] L. M. G. Fonseca, B. S. Manjunath. Registration Techniques for Multisensor Re-motely Sensed Imagery. Photogrammetric Engineering and Remote Sensing. 1996,62(9):1049–1056
[126] J. C. Tilton. Comparison of Registration Techniques for GOES Visible ImageryData. Proceedings of IRW, NASA GSFC. 1997, vol. 11, 133–136
[127] W. Eppler, D. Paglieroni, M. Louie, J. Hanson. GOES Landmark Positioning Sys-tem. Proceedings of SPIE International Symposium on Optical Science, Engineering,and Instrumentation. 1996, vol. 2812, 789–804
[128] J. Townshend, C. O. Justice, C. Gurney, J. McManus. The Impact of Misregistrationon Change Detection. IEEE Transactions on Geoscience and Remote Sensing. 1992,30(9):1054–1060
[129] X. Dai, S. Khorram. The E?ects of Image Misregistration on the Accuracy ofRemotely Sensed Change Detection. IEEE Transactions on Geoscience and RemoteSensing. 1998, 36(9):1566–10577
[130] C. A. Bernstein, L. N. Kanal, D. Lavin, E. C. Olson. A Geometric Approach toSubpixel Registration Accuracy. Computer Vision, Graphics, and Image Processing.1987, 40(3):334–360
[2]王耀南.卫星遥感图像处理与分析系统的新方法研究及其应用[博士后研究工作报告].长沙:国防科学技术大学, 1997
[3]周海芳.遥感图像并行处理算法的研究与应用[博士学位论文].长沙:国防科学技术大学, 2003
[4] R. C. Gonzalez, R. E. Woods.阮秋琦,阮宇智等译.数字图像处理(第二版).北京:电子工业出版社, 2007
[5] http://www.ermapper.com/
[6] http://www.pcigeomatics.com/products/products overview.html
[7] http://www.erdas.com/Products/ERDASProductInformation/tabid/84/currentid/1050/default.aspx
[8] http://www.ittvis.com/ProductServices/ENVI.aspx
[9] http://159.226.117.45/rsim/index.htm
[10] http://www.intergraph.com/global/cn/
[11]赵瑞珍.小波理论及其在图像、信号处理中的算法研究[博士学位论文].西安:西安电子科技大学, 2001
[12]费双波.小波去噪算法研究及小波硬件实现[硕士学位论文].北京:北京交通大学, 2006
[13] W. Sweldens. The Lifting Scheme: A Custom-Design Construction of BiorthogonalWavelets. Applied and Computational Harmonic Analysis. 1996, 3(2):186–200
[14] W. Sweldens. The Lifting Scheme: A Construction of Second Generation Wavelets.SIAM Journal of Mathematical Analysis. 1998, 29(2):511–546
[15] S. G. Mallat. A Theory for Multiresolution Signal Decomposition: The WaveletRepresentation. IEEE Transactions on Pattern Analysis and Machine Intelligence.1989, 11(7):674–693
[16] W. B. Pennebaker, J. L. Mitchell. JPEG: Still Image Data Compression Standard.NY: Van Nostrand Reinhold, 1992
[17] M. Boliek, C. Christopoulos, E. Majani. JPEG2000 Part I Final Committee DraftVersion 1.0. ISO/IEC JTC1/SC29 WG1-2000, 2000
[18] David S. Taubman, Michael W. Marcellin.魏江力,柏正尧等译. JPEG2000图像压缩基础、标准和实践.北京:电子工业出版社, 2004
[19]小野定康,铃木纯司.强增福译. JPEG2000技术.北京:科学出版社, 2004
[20] http://www.altera.com.cn/
[21] http://china.xilinx.com/index.shtml
[22]李弼程,彭天强,彭波等.智能图像处理技术.北京:电子工业出版社, 2004
[23] M. Srikar, S. Srinivasan. A Novel Architecture for Lifting-Based DiscreteWaveletTransform for JPEG2000 Standard suitable for VLSI implementation. Proceedingsof the 16th International Conference on VLSI Design (VLSI’03). 2003, 202–207
[24] Chung-Jr Lian, Kuan-Fu Chen, HongHui Chen, Liang-Gee Chen. Analysis and Ar-chitecture Design of Lifting Based DWT and EBCOT for JPEG2000. Proceedings ofthe 2001 International Symposium on VLSI Technology, Systems, and Applications(VLSI-TSA’01). 2001, 180–183
[25] Jen-Shiun Chiang, Chih-Hsien Hsia, Hsin-Jung Chen, Te-Jung Lo. VLSI Architec-ture of Low Memory and High Speed 2D Lifting-Based Discrete Wavelet Transformfor JPEG2000 Applications. Proceedings of the 2005 IEEE International Symposiumon Circuits and Systems (ISCAS’05). 2005, vol. 5, 4554–4557
[26] S. Barua, J.E. Carletta, K.A. Kotteri, A.E. Bell. An E?cient Architecture forLifting-based Two-dimensional Discrete Wavelet Transforms. Integration, the VLSIJournal. 2005, 38(3):341–352
[27] K. Andra, C. Chakrabarti, T. Acharya. A VLSI Architecture for Lifting-basedForward and Inverse Wavelet Transform. IEEE Transactions on Signal Processing.2002, 50(4):966–977
[28] K. Andra, C. Chakrabarti, T. Acharya. A VLSI architecture for lifting-based wavelettransform. Proceedings of the 2000 IEEE Workshop on Signal Processing Systems(SIPs’00). 2000, 70–79
[29]董文辉,刘明业. JPEG2000小波提升算法的硬件设计.电子学报. 2003,31(11):1674–1677
[30] G. Kuzmanov, G. Gaydadjiev, S. Vassiliadis. Multimedia Rectangularly AddressableMemory. IEEE Transactions on Multimedia. 2006, 8(2):315–322
[31] J. W. Park. An E?cient Bu?er Memory System for Subarray Access. IEEE Trans-actions on Parallel and Distributed Systems. 2001, 12(3):316–335
[32] G. Kuzmanov, S. Vassiliadis, J. Van Eijndhoven. A 2D Addressing Mode for Mul-timedia Applications. Proceedings of 2001 International Symposium on Systems,Architectures, Modeling, and Simulation (SAMOS’01). 2001, vol. 2268, 291–306
[33] M. B. Haverkamp, G. Kuzmanov, S. Vassiliadis. Implementing 2D Memory Bu?ersfor MPEG. Proceedings of PRORISC’03). 2003, 90–94
[34] Chung-Jr Lian, Kuan-Fu Chen, Hong-Hui Chen, Liang-Gee Chen. Analysis and Ar-chitecture Design of Block-coding Engine for EBCOT in JPEG2000. IEEE Trans-actions on Circuits and Systems for Video Technology. 2003, 13(3):219–230
[35]杨珂,刘明业. JPEG2000系数位平面编码的FPGA设计.计算机应用研究.2005, (10):157–161
[36] K. Andra, C. Chakrabarti, T. Acharya. A High-Performance JPEG2000 Archi-tecture. IEEE Transactions on Circuits and Systems for Video Technology. 2003,13(3):209–218
[37] A. K. Gupta, D. Taubman, S. Nooshabadi. High-speed VLSI Architecture for BitPlane Encoder of JPEG2000. Proceedings of the 47th IEEE International MidwestSymposium on Circuits and Systems (MWSCAS’04). 2004, vol. 2, 341–352
[38] A. K. Gupta, S. Nooshabadi, D. Taubman. Optimal 2 Sub-bank Memory Architec-ture for Bit Plane Coder of JPEG2000. Proceedings of the 2005 IEEE InternationalSymposium on Circuits and Systems (ISCAS’05). 2005, vol. 5, 4373–4376
[39]赵兴,沈海斌,阳晔.一种新型的样本并行JPEG2000编码器EBCOT体系.视频技术应用与工程. 2005, (4):99–102
[40] Zhao Xing, Yang Ye, Qin Xing, Wu Tuo, Shen Hai-Bin. A Cycle-E?cient Sample-Parallel EBCOT Architecture for JPEG2000 Encoder. Proceedings of the 2004International Symposium on Intelligent Multimedia, Video and Speech Processing(ISIMP’04). 2004, 386–389
[41] Jen-Shiun Chiang, Yu-Sen Lin, Chang-Yo Hsieh. E?cient Pass-Parallel Architecturefor EBCOT in JPEG2000. Proceedings of the 2002 IEEE International Symposiumon Circuits and Systems (ISCAS’02). 2002, vol. 1, I–773–I–776
[42] Jen-Shiun Chiang, Chun-Hau Chang, Yu-Sen Lin, Chang-Yuo Hsieh. High Through-put Rate EBCOT Architecture for JPEG2000. Proceedings of the 46th IEEE Inter-national Midwest Symposium on Circuits and Systems (MWSCAS’03). 2003, vol. 2,610–613
[43] Jen-Shiun Chiang, Chun-Hau Chang, Yu-Sen Lin, Chang-You Hsieh, Chih-HsiehHsia. High-speed EBCOT with Dual Context-modeling Coding Architecture forJPEG2000. Proceedings of the the 2004 International Symposium on Circuits andSystems (ISCAS’04). 2004, vol. 3, 865–868
[44]刘凯,李云松,吴成柯.高性能的EBCOT编码及其VLSI结构.软件学报. 2006,17(7):1553–1560
[45]刘凯,吴成柯,李云松,庄怀宇.比特平面并行的EBCOT编码及其VLSI结构.计算机学报. 2004, 27(7):928–935
[46] Liu Kai, Wu Chengke, Li Yunsong. A High-Performance VLSI Architecture ofEBCOT Block Coding in JPEG2000. Journal of Electronics. 2006, 23(1):89–93
[47] H.-C. Fang, T.-C. Wang, C.-J. Lian, T.-H. Chang, L.-G. Chen. Parallel EmbeddedBlock Coding Architecture for JPEG 2000. IEEE Transactions on Circuits andSystems for Video Technology. 2005, 15(9):1086–1097
[48] L.-F. Chen, T.-L. Huang, T.-M. Chou, Y.-K. Lai. Analysis and Architecture Designfor Memory E?cient Parallel Embedded Block Coding in JPEG2000. Proceedings ofthe 2006 IEEE International Conference on Acoustics, Speech, and Signal Processing(ICASSP’06). 2006, vol. 3, III–964–III–967
[49] Y. Li, R. E. Aly, M. A. Bayoumi, S. A. Mashali. Parallel Highspeed Architecturefor EBCOT in JPEG2000. Proceedings of the 2003 IEEE International Conferenceon Acoustics, Speech, and Signal Processing (ICASSP’03). 2003, vol. 2, 481–484
[50] Y. Li, M. Bayoumi. Three-level Parallel High Speed Architecture for EBCOT inJPEG2000. Proceedings of the 2005 IEEE International Conference on Acoustics,Speech, and Signal Processing (ICASSP’05). 2005, vol. 5, 5–8
[51] Y. Li, M. Bayoumi. A Three-level Parallel High-speed Low-power Architecturefor EBCOT of JPEG2000. IEEE Transactions on Circuits and Systems for VideoTechnology. 2006, 16(9):1153–1163
[52] H.-C. Fang, Y.-W. Chang, C.-C. Cheng, C.-C. Chen, L.-G. Chen. Memory E?cientJPEG 2000 Architecture with Stripe Pipeline Scheme. Proceedings of the 2005 IEEEInternational Conference on Acoustics, Speech, and Signal Processing (ICASSP’05).2005, vol. 5, 1–4
[53]韩彦菊,许超. JPEG2000分数位平面编码器的FPGA电路实现.计算机工程.2005, 31(15):183–185
[54]许超,韩彦菊.一种快速的分数位平面编码方法及其电路结构.计算机学报.2005, 28(10):1650–1656
[55] Bao-Feng Li, Yong Dou. Subblock-based BPE Scheme to Conquer Mismatch inMemory Access Pattern. Proceedings of the 2008 IEEE International Conferenceon Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP’08).2008, 814–817
[56] C. R. Castro-Pareja, R. Shekhar. Hardware Acceleration of Mutual Information-Based 3D Image Registration. Journal of Imaging Science and Technology. 2005,49(2):105–113
[57] C.R. Castro-Pareja, J. M. Jagadeesh, R. Shekhar. FAIR: A Hardware Architecturefor Real-Time 3D Image Registration. IEEE Transactions on Information Technol-ogy in Biomedicine. 2003, 7(4):426–434
[58]肖莎,尹酉,刘江沙.基于小波变换的图像增强技术.科技咨询导报. 2007,2:45–46
[59]王欣,王云霄,于晓,庞云阶.基于多小波阈值收缩与子带增强的图像去噪.哈尔滨工业大学学报. 2008, 40(1):152–154
[60] T. J. Brown. An Adaptive Strategy for Wavelet Based Image Enhancement. Pro-ceedings of 2000 Irish Machine Vision and Image Processing Conference. 2000, 67–81
[61]王惠,王宇,蔡光程.基于小波阈值的图像增强算法.电脑知识与技术. 2007,23:1405–1408
[62]陈志坚,成培,李峰.基于小波变换的多层次图像增强算法.长沙理工大学学报. 2006, 3(2):69–72
[63] R. J. Claudio, S. Jacob. Adaptive Image Denosing and Edge Enhancement in Scale-space Using the Wavelet Transform. Pattern Recognition Letters. 2003, 24(7):965–971
[64]刘劲,康志伟,何怡刚.基于小波变换的反锐化掩模图像增强新算法.计算机工程. 2007, 33(21):179–181
[65] J. G. Leu. Edge Sharpening Through Ramp Width Reduction. Image and VisionComputing. 2000, 18(6):510–514
[66]徐凌,刘薇,杨光.结合小波域变换和空间域变换的图像增强方法.波谱学杂志. 2007, 24(4):462–468
[67]陈帅.基于小波分析的遥感图像增强研究[硕士学位论文].成都:成都理工大学, 2006
[68]高飞,杨平先,孙兴波.基于小波变换与阀值收缩法的图像增强去噪.四川理工学院学报. 2006, 19(2):8–11
[69]赵同阳,张晓帆,周可法,徐仕琪.小波变换在遥感图像降噪中的应用.中国科技信息. 2007, 1:248–250
[70]武文波,张迅,陈静.小波阈值的改进及在遥感图像去噪中的应用.遥感技术与应用. 2007, 22(6):722–726
[71]赵继印,郝志成,李建坡.小波自适应比例改进算法在图像去噪中的应用.光电工程. 2006, 33(1):81–84
[72] M. K. Mihcak, I. Kozintsev, K. Ramchandran, P. Moulin. Low-Complexity ImageDenoising Based on Statistical Modeling of Wavelet Coe?cients. IEEE SIGNALPROCESSING LETTERS. 1999, 6(12):300–303
[73] M. K. Mihcak, I. Kozintsev, K. Ramchandran. Spatially Adaptive Statistical Mod-eling of Wavelet Image Coe?cients and Its Application to Denoising. Proceed-ings of 1999 International Conference on Acoustics,Speech and Signal Processing(ICASSP’99). 1999, 3253–3256
[74] J. M. Shapiro. Embedded Image Coding Using Zerotrees of Wavelet Coe?cients.IEEE Transactions on Signal Processing. 1993, 41(12):3445–3462
[75] A. Said, W. A. Pearlman. A New, Fast, and E?cient Image Codec Based on SetPartitioning in Hierarchical Trees. IEEE Transactions on Circuits and Systems forVideo Technology. 1996, 6(3):243–250
[76] Krishnaraj M. Varma. Fast Split Arithmetic Encoder Architectures and PerceptualCoding Methods for Enhanced JPEG2000 Performance [Ph.D Thesis]. Blacksburg:Virginia Polytechnic Institute and State University, 2006
[77] D. Taubman. High Performance Scalable Image Compression with EBCOT. IEEETransactions on Image Processing. 2000, 9(7):1158–1170
[78] L. G. Brown. A Survey of Image Registration Techniques. ACM Computing Surveys.1992, 24(4):325–376
[79] B. Zitova, J. Flusser. Image Registration Methods: A Survey. Image and VisionComputing. 2003, 21(11):977–1000
[80] J. P. Djamdji, A. Bijaoui, R. Maniere. Geometrical Registration of Images: The Mul-tiresolution Approach. Photogrammetric Engineering and Remote Sensing. 1993,59(5):645–653
[81] J. Flusser. An Adaptive Method for Image Registration. Pattern Recognition. 1992,25(1):45–54
[82] B. S. Manjunath, C. Shekhar, R. Chellappa. A New Approach to Image FeatureDetection with Applications. Pattern Recognition. 1996, 29(4):627–640
[83] A.D. Ventura, A. Rampini, R. Schettini. Image Registration by The Recognition ofCorresponding Structures. IEEE Transactions on Geoscience and Remote Sensing.1990, 28(5):305–387
[84] Q. Zheng, R. Chellappa. A Computational Vision Approach to Image Registration.IEEE Transactions on Image Processing. 1993, 2(7):311–326
[85] H. Li, B. S. Manjunath, S. K. Mitra. A Contour-based Approach to MultisensorImage Registration. IEEE Transactions on Image Processing. 1995, 4(3):320–334
[86] J. P. Djamdji, A. Bijaoui. Disparity Analysis: A Wavelet Transform Approach.IEEE Transactions on Geoscience and Remote Sensing. 1995, 33(1):67–76
[87] H. H. Li, Y. T. Zhou. Wavelet-based Point Feature Extractor for Multisensor Im-age Registration. Proceedings of SPIE Aerosense Wavelet Applications III. 1996,2762:524–534
[88] Y.C. Hsieh, D.M. McKeown. Performance Evaluation of Scene Registration andStereo Matching for Cartographic Feature Extraction. IEEE Transactions on Pat-tern Analysis and Machine Intelligence. 1992, 14(2):214–238
[89] J. L. Moigne, W. J. Campbell, R. F. Cromp. An Automated Image RegistrationTechnique Based on The Correlation of Wavelet Features. IEEE Transactions onGeoscience and Remote Sensing. 2002, 40(8):1849–1864
[90] J. L. Moigne, A. Cole-Rhodes, R. Eastman. Multi-sensor Registration of EarthRemotely Sensed Imagery. Proceedings of the 8th SPIE International Symposiumon Remote Sensing. 2001, vol. 4541, 1–10
[91] J. L. Moigne, I. Zavorin. Use of Wavelets for Image Registration. Proceedings ofthe SPIE Aerosense 2000, Wavelet Applications VII. 2000, vol. 4056, 99–108
[92] P. Thevenaz, U. E. Ruttimann, M. Unser. A Pyramid Approach to Sub-pixel Regis-tration Based on Intensity. IEEE Transactions on Image Processing. 1998, 7(1):27–41
[93] R. L. Allen, F. A. Kamangar, E.M. Stokely. Laplacian and Orthogonal WaveletPyramid Decompositions in Coarse-to-fine Registration. IEEE Transactions on Sig-nal Processing. 1993, 41(12):3536–3541
[94] Q. S. Chen, M. Defrise, F. Deconinck. Symmetric Phase-Only Matched Filtering ofFourier-Mellin Transforms for Image Registration and Recognition. IEEE Transac-tions on Pattern Analysis and Machine Intelligence. 1994, 16(12):1156–1168
[95] J. C. Olivo, J. Deubler, C. Boulin. Automatic Registration of Images by a Wavelet-based Multiresolution Approach. Proceedings of SPIE Wavelet Applications in Sig-nal and Image Processing III. 1995, vol. 2569, 234–244
[96] H. Shekarforoush, M. Berthod, J. Zerubia. Subpixel Image Registration by Estimat-ing the Polyphase Decomposition of the Crosspower Spectrum. Proceedings of 1996IEEE Conference on Computer Vision and Pattern Recognition. 1996, 532–537
[97] R. C. Hardie, K. J. Barnard, E. E. Armstrong. Joint MAP Registration and High-Resolution Image Estimation Using a Sequence of Undersampled Images. IEEETransactions on Image Processing. 1997, 6(12):1621–1633
[98] R. J. Althof, M.G.J. Wind, J.T. Dobbins. A Rapid and Automatic Image Regis-tration Algorithm with Subpixel Accuracy. IEEE Transactions on Medical Imagine.1997, 16(6):308–316
[99] H. F. Zhou, X. J. Yang, H. Z. Liu. First Evaluation of Parallel Methods of AutomaticGlobal Image Registration Based on Wavelets. Proceedings of the 2005 InternationalConference on Parallel Processing (ICPP’05). 2005, 129–136
[100] H. F. Zhou, Y. Tang, X. Yang. Research on Grid-Enabled Parallel Strategies ofAutomatic Wavelet-based Registration of Remote-Sensing Images and Its Applica-tion in ChinaGrid. Proceedings of the 4th International Conference on Image andGraphics (ICIG’07). 2007, 725–730
[101]敬忠良,肖刚,李振华.图像融合:理论与应用.北京:高等教育出版社, 2007
[102] D. A. Yocky. Image Merging and Data Fusion by Means of the Discrete Two-Dimensional Wavelet Transform. Journal of the Optical Society of America A:Optics, Image Science, and Vision. 1995, 12(9):1834–1841
[103] J. Nunez, X. Otazu, O. Fors, A. Prades, V. Pala, R. Arbiol. Multiresolution-basedImage Fusion with Additive Wavelet Decomposition. IEEE Transactions on Geo-science and Remote Sensing. 1999, 37(3):1204–1211
[104]陶观群,李大鹏,陆光华.基于小波变换的不同融合规则的图像融合研究.红外与激光工程. 2003, 32(2):173–176
[105]陶冰洁,王敬儒.采用小波分析的图像融合方法评述.计算机工程与应用.2005, 41(25):16–19
[106]强赞霞,彭嘉雄,王洪群.基于小波变换局部方差的遥感图像融合.华中科技大学学报. 2003, 31(6):89–91
[107]王国峰,张科,李言俊.基于小波多分辨分析的图像融合方法.红外与激光工程. 2003, 32(5):513–515
[108]霍宏涛,游先祥.小波变换在遥感图像融合中的应用研究.中国图像图形学报. 2003, 8A(5):551–556
[109] Ke Zhu, Xiao-Fang Zhou, Lin Hua, Qian-Ling Zhang. An E?cient VLSI Implemen-tation of DWT for JPEG2000. Proceedings of the 2003 International Conference onNeural Networks and Signal Processing (ICNNSP’03). 2003, vol. 2, 990–993
[110] http://www.openjpeg.org/
[111] C. Christopoulos, A. Skodras, T. Evrahimi. The JPEG2000 Still Image Coding Sys-tem: An Overview. IEEE Transactions on Consumer Electronics. 2000, 46(4):1103–1127
[112] K.C.B. Tan, T. Arslan. An Embedded Extension Algorithm for the Lifting BasedDiscrete Wavelet Transform in JPEG2000. Proceedings of the 2002 IEEE Interna-tional Conference on Acoustics, Speech, and Signal Processing (ICASSP’02). 2002,vol. 4, IV–3513–IV–3516
[113] K.C.B. Tan, T. Arslan. Low power embedded extension algorithm for lifting-baseddiscretewavelet transform in JPEG2000. Electronics Letters. 2001, 37(22):1328–1330
[114] C. Chrysafis, A. Ortega. Line-based, Reduced Memory, Wavelet Image Compression.IEEE Transactions on Image Processing. 2000, 9(3):378–389
[115]兰旭光,郑南宁,梅魁志,刘跃虎. JPEG2000并行阵列式小波滤波器的VLSI结构设计.电子学报. 2004, 32(11):1806–1809
[116] G. Pastuszak. A Novel Architecture of Arithmetic Coder in JPEG2000 Based onParallel Symbol Encoding. Proceedings of the 2004 IEEE International Conferenceon Parallel Computing in Electrical Engineering (PARELEC’04). 2004, 303–308
[117] Jen-Shiun Chiang, Chang-Yo Hsieh, Jin-Chan Liu, Cheng-Chih Chien. ConcurrentBit-Plane Coding Architecture for EBCOT in JPEG2000. Proceedings of the 2006International Symposium on Circuits and Systems (ISCAS’06). 2006, vol. 5, 4595–4598
[118]王勇,郑南宁,梅魁志,朱悦心,刘跃虎.一种高效的JPEG2000位平面编码器设计.西安交通大学学报. 2005, 39(2):158–161
[119] Y.-Z. Zhang, C. Xu, L.-B. Chen. A Dual-Symbol Coding Arithmetic Coder Archi-tecture Design for High Speed EBCOT Coding Engine in JPEG2000. Proceedingsof the 2005 International Conference on ASIC (ASICON’05). 2005, vol. 1, 261–264
[120] M. Dyer, D. Taubman, S. Nooshabadi, A. K. Gupta. Concurrency Techniques forArithmetic Coding in JPEG2000. IEEE Transactions on Circuits and Systems I:Regular Papers. 2006, 53(6):1203–1213
[121] Yu-Wei Chang, Hung-Chi Fang, Liang-Gee Chen. High Performance Two-SymbolArithmetic Encoder in JPEG2000. Proceedings of the 2004 IEEE InternationalSymposium on Consumer Electronics (ISCE’04). 2004, 101–104
[122] N. Noikaew, O. Chitsobhuk. Dual Symbol Processing for MQ Arithmetic Coder inJPEG2000. Proceedings of the 2008 IEEE Congress on Image and Signal Processing(CISP’08). 2008, vol. 1, 521–524
[123] Y. Li, M. Elgamel, M. Bayoumi. A Partial Parallel Algorithm and Architecturefor Arithmetic Encoder in JPEG2000. Proceedings of the 2005 IEEE InternationalSymposium on Circuits and Systems (ISCAS’05). 2005, vol. 5, 5198–5201
[124] G. Wolberg, S. Zokai. Image Registration for Perspective Deformation Recovery.Proceedings of the SPIE Conference on Autonatic Target Recognition X. 2000, 1–12
[125] L. M. G. Fonseca, B. S. Manjunath. Registration Techniques for Multisensor Re-motely Sensed Imagery. Photogrammetric Engineering and Remote Sensing. 1996,62(9):1049–1056
[126] J. C. Tilton. Comparison of Registration Techniques for GOES Visible ImageryData. Proceedings of IRW, NASA GSFC. 1997, vol. 11, 133–136
[127] W. Eppler, D. Paglieroni, M. Louie, J. Hanson. GOES Landmark Positioning Sys-tem. Proceedings of SPIE International Symposium on Optical Science, Engineering,and Instrumentation. 1996, vol. 2812, 789–804
[128] J. Townshend, C. O. Justice, C. Gurney, J. McManus. The Impact of Misregistrationon Change Detection. IEEE Transactions on Geoscience and Remote Sensing. 1992,30(9):1054–1060
[129] X. Dai, S. Khorram. The E?ects of Image Misregistration on the Accuracy ofRemotely Sensed Change Detection. IEEE Transactions on Geoscience and RemoteSensing. 1998, 36(9):1566–10577
[130] C. A. Bernstein, L. N. Kanal, D. Lavin, E. C. Olson. A Geometric Approach toSubpixel Registration Accuracy. Computer Vision, Graphics, and Image Processing.1987, 40(3):334–360
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |