基于编码感知的高分辨率计算成像方法研究
|
摘要
高分辨率多维信息获取技术研究是国家中长期科学和技术发展规划的任务之一。高分辨率探测成像是其中一个重要的研究和探索方向，尤其在航天遥感、医疗诊断、军事侦查等领域高分辨成像需求迫切。
传统成像方法通过在传感器像元与场景之间建立一种直接的一一对应关系来获取图像，成像效果严重依赖传感器性能（像元密度，灵敏度等），且存在二维传感器阵列无法实现高分辨多维信息（空间-谱间-时间）快速获取等问题。如何利用普通探测器获得高分辨率图像是众多学者们面临的挑战。
近年来蓬勃发展起来的计算成像方法，尤其是基于压缩感知理论的计算成像技术，为多维信息高分辨率成像方法带来了新的机遇，受到了国内外学者的广泛关注。本文在此研究现状和背景下，以863计划、国家自然科学基金及中央高校基本科研重点项目为研究平台，以压缩感知理论为基础，围绕编码感知高分辨率计算成像展开研究，从“编码、混叠、反演”等关键环节，对灰度图像、彩色图像、遥感光谱图像的成像方法分别进行了研究，旨在摆脱成像分辨率对高性能探测器的严重依赖。
本文主要工作和创新点如下：
1、针对传统成像方法分辨率严重依赖相机传感器阵列密度的问题，提出一种基于运动随机曝光的高分辨率编码感知计算成像新方法。在编码感知阶段，设计了运动随机曝光模式，用一个较低分辨相机相对场景运动，在运动的同时通过随机编码序列控制快门闪动实现高速曝光，运动结束后完成场景的编码混叠采样；在优化反演阶段，利用场景的先验知识，构建相应的稀疏重建模型，重构出高分辨率图像。数据仿真实验和半实物仿真实验结果均表明，该成像方法能够实现较低分辨率相机的高分辨率成像，有效解决了传统方法成像分辨率严重依赖相机传感器阵列密度的问题，特别适用于由于成本、功耗、体积、重量、存储与传输等限制而无法使用高性能传感器的场合，如星载对地观测遥感成像。
2、针对传统Demosaicking（去马赛克）方法存在重建效果不理想、拉链虚假色现象严重等问题，作者从编码感知计算成像的角度重新审视彩色图像Demosaicking过程，提出了一种基于图像稀疏模型和自适应PCA（PrincipalComponent Analysis）的Demosaicking方法。该方法通过彩色图像成像模型和光谱特征来描述各颜色分量间稀疏性，通过自适应PCA来挖掘空间维稀疏性，并将二者结合，建立了1-范数最小优化正则模型，并设计了优化求解算法。仿真实验结果表明该方法能提高重建全彩色图像的抗噪能力，结果优于大多数现有Demosaicking算法，实际成像结果也显示出了更好的视觉效果。
3、针对星载（机载）遥感推扫式光谱成像中空间分辨率和光谱分辨率受制于传感器阵列点阵密度的问题，及空间分辨率和光谱分辨率难以兼得的问题，根据编码感知计算成像框架，提出了一种基于高速快门闪动的高分辨率遥感光谱计算成像新方法。该方法在不改变相机结构和传感器密度的条件下，通过控制高速快门闪动来进行随机编码曝光，产生混叠效应，实现多个像素点与一个传感器像元对应，将丰富的场景信息压缩采样到少量的观测数据中。最后利用优化反演方法对混叠采样信号进行处理，重构出高分辨率图像。实验结果表明该方法无需提高传感器阵列密度，只需控制电子快门曝光和数据处理，就可以做到在保持较高光谱分辨率的前提下，较大提升光谱图像的空间分辨率。
4、针对上述方法中由于快门闪动遮挡一部分光线，从而减少了曝光时间，导致信噪比下降及部分场景信息丢失的问题，本文设计了另一种随机编码曝光方式，提出了基于反射角高速切换的编码曝光方法。该方法采用一个高速转镜将入射光分为两路，同时进行互补随机编码曝光调制，这样场景中所有像素全部参与了混叠，保留了足够的信息。最后利用场景稀疏性构建优化反演模型，重构出高分辨率光谱图像。实验结果也表明了该方法的优越性。
Technology of multi-dimension information acquisition is one of the tasks of thenational medium and long-term program for science and technology development. Highresolution imaging is an important research and exploration direction.Multi-dimensional high-resolution images are highly desired in many fields, such asspace remote sensing, medical diagnosis and military reconnaissance, etc.
In the traditional imaging method, an image is acquired directly from the scene inthe spatial domain. There exists one-to-one relationship between the scene and theimage. The resolution of the image is equal to the number of sensors in the imager.Therefore, because of the limitation of imaging principle, spatial resolution and spectralresolution of the imaging system are restricted to the density and sensitivity of thespectral detector. It is difficult to obtain simultaneous multi-dimensional high-resolutionimages in the traditional imaging method. Many scholars have to face the challenge tofulfill high resolution imaging with low-resolution.
As a rapidly developed technology in recent years, computational imaging, andcompressive sensing bring new hope for multi-dimensional high-resolution imaging.They have received extensive attention from scholars at home and abroad. Thisdissertation, under the support of “863” program, the national science foundation ofchina, fundamental research funds for the central universities of china, focuses onhigh-resolution computational imaging based on coded sensing, and mainly studies thecoded sensing methods of gray images, RGB full-color images and remote sensingspectral images.
The main contributions and innovation points of the thesis are as follows:
(1) Aiming at the problem of image spatial resolution restricted to the density of adetector, we propose a high-resolution imaging method via moving random exposure. Inour method, the image acquisition is performed in two stages, coded sensing(compressive measurement) and optimization reconstruction. In the first stage, alow-resolution camera along with the aircraft moves relative to the scene. compressivemeasurement are made by the low-resolution camera with randomly fluttering shutter,which can be viewed as a moving random exposure pattern. In the optimizationreconstruction stage, the HR image is computed by different models according to theprior knowledge of scenes. The simulation results demonstrate the effectiveness of theproposed imaging method. The proposed imaging method offers a new way of acquiring HR images of essentially static scenes with low-resolution, which is particularlysuitable for the occasions where the camera resolution is limited by severe constraintssuch as cost, battery capacity, size, memory space, transmission bandwidth, etc.
(2) There exist some problems such as the low quality, staircase artifacts (zippereffects) in the traditional methods. This thesis reexamines the color demosaickingproblem in a perspective of sparsity-driven image restoration, and propose a new colordemosaicking with an image sparse model and adaptive PCA (Principal ComponentAnalysis). Our method describes the sparsity within each color component by imageformation model and spectral feature, excavates spatial sparsity by adaptive PCA. Thespectral sparse representation is derived from a physical image formation model; thespatial sparse representation is based on a windowed adaptive principal componentanalysis. So, our method exploits spectral and spatial sparse representations of naturalimages jointly, and further proposes an minimization technique for reconstruction.The simulation results demonstrate that our method outperforms many existingtechniques by a large margin in PSNR and achieves higher visual quality.
(3) Aiming at the problems that spatial resolution and spectral resolution arerestricted to the density of the spectral detector and usually cannot be acquiredsimultaneously. This thesis, based on our coded sensing computational imagingframework, presents a high-resolution computational spectral imaging method ofremote sensing. The new image acquisition system employs a fluttering shuttercontrolled by a random sequence to modulate exposure to an ordinary imaging sensor,without change of mechanical structure and without increasing the density of originalimaging detector. It enables multiple scene pixel intensity accumulated in the samesensor pixel. Aliasing effect is produced. The optimization inversion algorithm based oncompressive sensing theory is used to reconstruct high-resolution multiple-spectralimage. Simulation results demonstrate that our method can greatly enhance the spatialresolution and keep high spectral resolution simultaneously without increasing densityof original imaging detector.
(4) In the above spectral imaging method, the use of coded exposure shutter in anoptical system sacrifices the amount of exposure because a fluttering shutter blockslight, resulting in a poor SNR. So we further design a new coded exposure strategywithout reducing exposure and present a new code exposure method based on highspeed switching of reflection angle. In the proposed system, we install the randomlyrotating mirror behind the slit of the imager. The method uses the randomly rotatingmirror to divide the incident light into two beams and fulfills random exposure complementary modulation. All pixels in a scene participate in the aliasing andsufficient information is collected. The high spatial resolution multi-spectral image canbe recovered by exploiting the signal sparsity. The recovery algorithm is based oncompressive sensing theory. Simulation results demonstrate the efficacy of the proposedtechnique.
传统成像方法通过在传感器像元与场景之间建立一种直接的一一对应关系来获取图像，成像效果严重依赖传感器性能（像元密度，灵敏度等），且存在二维传感器阵列无法实现高分辨多维信息（空间-谱间-时间）快速获取等问题。如何利用普通探测器获得高分辨率图像是众多学者们面临的挑战。
近年来蓬勃发展起来的计算成像方法，尤其是基于压缩感知理论的计算成像技术，为多维信息高分辨率成像方法带来了新的机遇，受到了国内外学者的广泛关注。本文在此研究现状和背景下，以863计划、国家自然科学基金及中央高校基本科研重点项目为研究平台，以压缩感知理论为基础，围绕编码感知高分辨率计算成像展开研究，从“编码、混叠、反演”等关键环节，对灰度图像、彩色图像、遥感光谱图像的成像方法分别进行了研究，旨在摆脱成像分辨率对高性能探测器的严重依赖。
本文主要工作和创新点如下：
1、针对传统成像方法分辨率严重依赖相机传感器阵列密度的问题，提出一种基于运动随机曝光的高分辨率编码感知计算成像新方法。在编码感知阶段，设计了运动随机曝光模式，用一个较低分辨相机相对场景运动，在运动的同时通过随机编码序列控制快门闪动实现高速曝光，运动结束后完成场景的编码混叠采样；在优化反演阶段，利用场景的先验知识，构建相应的稀疏重建模型，重构出高分辨率图像。数据仿真实验和半实物仿真实验结果均表明，该成像方法能够实现较低分辨率相机的高分辨率成像，有效解决了传统方法成像分辨率严重依赖相机传感器阵列密度的问题，特别适用于由于成本、功耗、体积、重量、存储与传输等限制而无法使用高性能传感器的场合，如星载对地观测遥感成像。
2、针对传统Demosaicking（去马赛克）方法存在重建效果不理想、拉链虚假色现象严重等问题，作者从编码感知计算成像的角度重新审视彩色图像Demosaicking过程，提出了一种基于图像稀疏模型和自适应PCA（PrincipalComponent Analysis）的Demosaicking方法。该方法通过彩色图像成像模型和光谱特征来描述各颜色分量间稀疏性，通过自适应PCA来挖掘空间维稀疏性，并将二者结合，建立了1-范数最小优化正则模型，并设计了优化求解算法。仿真实验结果表明该方法能提高重建全彩色图像的抗噪能力，结果优于大多数现有Demosaicking算法，实际成像结果也显示出了更好的视觉效果。
3、针对星载（机载）遥感推扫式光谱成像中空间分辨率和光谱分辨率受制于传感器阵列点阵密度的问题，及空间分辨率和光谱分辨率难以兼得的问题，根据编码感知计算成像框架，提出了一种基于高速快门闪动的高分辨率遥感光谱计算成像新方法。该方法在不改变相机结构和传感器密度的条件下，通过控制高速快门闪动来进行随机编码曝光，产生混叠效应，实现多个像素点与一个传感器像元对应，将丰富的场景信息压缩采样到少量的观测数据中。最后利用优化反演方法对混叠采样信号进行处理，重构出高分辨率图像。实验结果表明该方法无需提高传感器阵列密度，只需控制电子快门曝光和数据处理，就可以做到在保持较高光谱分辨率的前提下，较大提升光谱图像的空间分辨率。
4、针对上述方法中由于快门闪动遮挡一部分光线，从而减少了曝光时间，导致信噪比下降及部分场景信息丢失的问题，本文设计了另一种随机编码曝光方式，提出了基于反射角高速切换的编码曝光方法。该方法采用一个高速转镜将入射光分为两路，同时进行互补随机编码曝光调制，这样场景中所有像素全部参与了混叠，保留了足够的信息。最后利用场景稀疏性构建优化反演模型，重构出高分辨率光谱图像。实验结果也表明了该方法的优越性。
Technology of multi-dimension information acquisition is one of the tasks of thenational medium and long-term program for science and technology development. Highresolution imaging is an important research and exploration direction.Multi-dimensional high-resolution images are highly desired in many fields, such asspace remote sensing, medical diagnosis and military reconnaissance, etc.
In the traditional imaging method, an image is acquired directly from the scene inthe spatial domain. There exists one-to-one relationship between the scene and theimage. The resolution of the image is equal to the number of sensors in the imager.Therefore, because of the limitation of imaging principle, spatial resolution and spectralresolution of the imaging system are restricted to the density and sensitivity of thespectral detector. It is difficult to obtain simultaneous multi-dimensional high-resolutionimages in the traditional imaging method. Many scholars have to face the challenge tofulfill high resolution imaging with low-resolution.
As a rapidly developed technology in recent years, computational imaging, andcompressive sensing bring new hope for multi-dimensional high-resolution imaging.They have received extensive attention from scholars at home and abroad. Thisdissertation, under the support of “863” program, the national science foundation ofchina, fundamental research funds for the central universities of china, focuses onhigh-resolution computational imaging based on coded sensing, and mainly studies thecoded sensing methods of gray images, RGB full-color images and remote sensingspectral images.
The main contributions and innovation points of the thesis are as follows:
(1) Aiming at the problem of image spatial resolution restricted to the density of adetector, we propose a high-resolution imaging method via moving random exposure. Inour method, the image acquisition is performed in two stages, coded sensing(compressive measurement) and optimization reconstruction. In the first stage, alow-resolution camera along with the aircraft moves relative to the scene. compressivemeasurement are made by the low-resolution camera with randomly fluttering shutter,which can be viewed as a moving random exposure pattern. In the optimizationreconstruction stage, the HR image is computed by different models according to theprior knowledge of scenes. The simulation results demonstrate the effectiveness of theproposed imaging method. The proposed imaging method offers a new way of acquiring HR images of essentially static scenes with low-resolution, which is particularlysuitable for the occasions where the camera resolution is limited by severe constraintssuch as cost, battery capacity, size, memory space, transmission bandwidth, etc.
(2) There exist some problems such as the low quality, staircase artifacts (zippereffects) in the traditional methods. This thesis reexamines the color demosaickingproblem in a perspective of sparsity-driven image restoration, and propose a new colordemosaicking with an image sparse model and adaptive PCA (Principal ComponentAnalysis). Our method describes the sparsity within each color component by imageformation model and spectral feature, excavates spatial sparsity by adaptive PCA. Thespectral sparse representation is derived from a physical image formation model; thespatial sparse representation is based on a windowed adaptive principal componentanalysis. So, our method exploits spectral and spatial sparse representations of naturalimages jointly, and further proposes an minimization technique for reconstruction.The simulation results demonstrate that our method outperforms many existingtechniques by a large margin in PSNR and achieves higher visual quality.
(3) Aiming at the problems that spatial resolution and spectral resolution arerestricted to the density of the spectral detector and usually cannot be acquiredsimultaneously. This thesis, based on our coded sensing computational imagingframework, presents a high-resolution computational spectral imaging method ofremote sensing. The new image acquisition system employs a fluttering shuttercontrolled by a random sequence to modulate exposure to an ordinary imaging sensor,without change of mechanical structure and without increasing the density of originalimaging detector. It enables multiple scene pixel intensity accumulated in the samesensor pixel. Aliasing effect is produced. The optimization inversion algorithm based oncompressive sensing theory is used to reconstruct high-resolution multiple-spectralimage. Simulation results demonstrate that our method can greatly enhance the spatialresolution and keep high spectral resolution simultaneously without increasing densityof original imaging detector.
(4) In the above spectral imaging method, the use of coded exposure shutter in anoptical system sacrifices the amount of exposure because a fluttering shutter blockslight, resulting in a poor SNR. So we further design a new coded exposure strategywithout reducing exposure and present a new code exposure method based on highspeed switching of reflection angle. In the proposed system, we install the randomlyrotating mirror behind the slit of the imager. The method uses the randomly rotatingmirror to divide the incident light into two beams and fulfills random exposure complementary modulation. All pixels in a scene participate in the aliasing andsufficient information is collected. The high spatial resolution multi-spectral image canbe recovered by exploiting the signal sparsity. The recovery algorithm is based oncompressive sensing theory. Simulation results demonstrate the efficacy of the proposedtechnique.
引文
[1]张良培，沈焕锋，张洪艳.图像超分辨率重建[M].北京:科学出版社,2012.
[2] E. J. Candès. Compressive Sampling[C].Proceedings of the International Congress ofMathematicians, Madrid, Spain:2006.
[3] E. J. Candes, J. Romberg, T. Tao. Robust Uncertainty Principles: Exact SignalReconstruction From Highly Incomplete Frequency Information[J]. IEEE Trans. onInformation Theory.2006,52(2):489-509.
[4] E. J. Candes, J. Romberg. Quantitative Robust Uncertainty Principles and OptimallySparse Decompositions[J]. Foundations Of Computational Mathematics.2006,6(2):227-254.
[5] D. L. Donoho. Compressed Sensing[J]. IEEE Trans. on Information Theory.2006,52(4):1289-1306.
[6] Y. Tsaig, D. L. Donoho. Extensions of Compressed Sensing[J]. Signal Processing.2006,86(3):549-571.
[7] F. Durand, R. Szeliski. Computational Photography[J]. IEEE Comput Graph Appl.2007,27(2):21-22.
[8] R. Raskar. Less is More: Coded Computational Photography[M]. BERLIN:SPRINGER-VERLAG BERLIN,2007:4843,1-12.
[9] O. Bimber. Computational Photography-The Next Big Step[J]. Computer.2006,39(8):28-29.
[10] D. J. Brady. Optical Imaging and Spectroscopy[M]. Wiley-OSA,2009.
[11] J. L. Suo, X. Y. Ji, Q. H. Dai. An Overview of Computational Photography[J]. ScienceChina-Information Sciences.2012,55(6):1229-1248.
[12]徐树奎，涂丹，李国辉等.计算摄影综述[J].计算机应用研究.2010(11):4032-4039.
[13] J. Romberg. Imaging Via Compressive Sampling[J]. IEEE Signal Processing Magazine.2008,25(2):14-20.
[14] J. Romberg. Compressive Sensing by Random Convolution[J]. SIAM J. Img. Sci.2009,2(4):1098-1128.
[15] M. F. Duarte, M. A. Davenport, D. Takhar, et al. Single-Pixel Imaging Via CompressiveSampling[J]. IEEE Signal Processing Magazine.2008,25(2):83-91.
[16] M. B. Wakin, J. N. Laska, M. F. Duarte, et al. An Architecture for CompressiveImaging[C].2006IEEE International Conference on Image Processing,2006.
[17] D. Takhar, J. N. Laska, M. B. Wakin, et al. A New Compressive Imaging CameraArchitecture Using Optical-Domain Compression[J]. Computational Imaging IV atIS&T/SPIE Electronic Imaging.2006:43-52.
[18] C. Hongzhi, X. Ning, S. Bo, et al. Infrared Camera Using a Single Nano-Photodetector[J].IEEE Sensors Journal.2013,13(3):949-958.
[19] A. C. Fannjiang. Exact Localization and Superresolution with Noisy Data and RandomIllumination[J]. Inverse Problems.2013,2011(27):65012.
[20] A. Agrawal, R. Raskar. Resolving Objects at Higher Resolution From a SingleMotion-Blurred Image[C].IEEE Conference on Computer Vision and Pattern Recognition,CVPR '07.,2007.
[21] P. Nagesh, L. Baoxin. Compressive Imaging of Color Images[C].IEEE InternationalConference on Acoustics, Speech and Signal Processing, ICASSP2009,2009.
[22] B. Khademhosseinieh, G. Biener, I. Sencan, et al. Lensfree Color Imaging On aNanostructured Chip Using Compressive Decoding[J]. Applied Physics Letters.2010,97(21):211112.
[23] B. Khademhosseinieh, G. Biener, I. Sencan, et al. Lensless On-Chip Color Imaging UsingNano-Structured Surfaces and Compressive Decoding[C].2011Conference on Lasers andElectro-Optics (CLEO),2011.
[24] A. F. Coskun, T. Su, I. Sencan, et al. Lensfree Fluorescent On-Chip ImagingusingCompressive Sampling[J]. Opt. Photon. News.2010,21(12):27.
[25] A. Majumdar, R. K. Ward, T. Aboulnasr. Algorithms to Approximately Solve Np HardRow-Sparse Mmv Recovery Problem: Application to Compressive Color Imaging[J].IEEE Journal on Emerging and Selected Topics in Circuits and Systems.2012,2(3):362-369.
[26] A. Majumdar, R. K. Ward. Compressive Color Imaging with Group-Sparsity On AnalysisPrior[C].201017th IEEE International Conference on Image Processing (ICIP),2010.
[27] A. Wagadarikar, R. John, R. Willett, et al. Single Disperser Design for Coded ApertureSnapshot Spectral Imaging[J]. Applied Optics.2008,47(10): B44-B51.
[28] M. E. Gehm, R. John, D. J. Brady, et al. Single-Shot Compressive Spectral Imaging witha Dual-Disperser Architecture[J]. Opt. Express.2007,15(21):14013-14027.
[29] P. Ye, H. Arguello, G. Arce. Spectral Aperture Code Design for Multi-Shot CompressiveSpectral Imaging[C].Optical Society of America,2010.
[30] A. Mrozack, D. L. Marks, D. J. Brady. Coded Aperture Spectroscopy with Denoisingthrough Sparsity[J]. Opt. Express.2012,20(3):2297-2309.
[31] R. M. Willett, M. E. Gehm, D. J. Brady. Multiscale Reconstruction for ComputationalSpectral Imaging [C].SPIE-INT SOC OPTICAL ENGINEERING, BELLINGHAM:SPIE-INT SOC OPTICAL ENGINEERING,2007.
[32] A. A. Wagadarikar, N. P. Pitsianis, X. Sun, et al. Video Rate Spectral Imaging Using aCoded Aperture Snapshot Spectral Imager[J]. Optics Express.2009,17(8):6368-6388.
[33] S. Xianbiao, N. Ahuja. Imaging Via Three-Dimensional Compressive Sampling(3Dcs)[C].2011IEEE International Conference on Computer Vision (ICCV),2011.
[34] A. C. Sankaranarayanan, P. K. Turaga, R. G. Baraniuk, et al. Compressive Acquisition ofDynamic Scenes[C].ECCV'10, Berlin, Heidelberg: Springer-Verlag,2010.
[35] Y. Hitomi, G. Jinwei, M. Gupta, et al. Video From a Single Coded Exposure PhotographUsing a Learned Over-Complete Dictionary[C].2011IEEE International Conference onComputer Vision (ICCV),2011.
[36] R. F. Marcia, R. M. Willett. Compressive Coded Aperture Superresolution ImageReconstruction[C].IEEE International Conference on Acoustics, Speech and SignalProcessing, ICASSP2008,2008.
[37] X. Jie, M. Jianwei, Z. Dongming, et al. Compressive Video Sensing Based On UserAttention Model[C].Picture Coding Symposium (PCS2010),2010.
[38] Y. P. Jae, M. B. Wakin. A Multiscale Framework for Compressive Sensing ofVideo[C].Picture Coding Symposium, PCS2009,2009.
[39] M. B. Wakin, J. N. Laska, M. F. Duarte, et al. Compressive Imaging for VideoRepresentation and Coding[C].Proceedings of Picture Coding Symposium (PCS2006),2006.
[40] X. Siyuan, C. Lin. Scalable Video Coding with Compressive Sensing for WirelessVideocast[C].2011IEEE International Conference on Communications (ICC),2011.
[41] S. Pudlewski, A. Prasanna, T. Melodia. Compressed-Sensing-Enabled Video Streamingfor Wireless Multimedia Sensor Networks[J]. IEEE Trans. on Mobile Computing.2012,11(6):1060-1072.
[42] S. Pudlewski, T. Melodia. A Rate-Energy-Distortion Analysis for Compressed-Sensing-Enabled Wireless Video Streaming On Multimedia Sensors[C].2011IEEE GlobalTelecommunications Conference (GLOBECOM2011),2011.
[43] L. Sungwon, A. Ortega. Adaptive Compressed Sensing for Depthmap Compression UsingGraph-Based Transform[C].201219th IEEE International Conference on ImageProcessing (ICIP),2012.
[44] G. Coluccia, E. Magli. A Novel Progressive Image Scanning and Reconstruction SchemeBased On Compressed Sensing and Linear Prediction[C].2012IEEE InternationalConference on Multimedia and Expo (ICME),2012.
[45] G. Coluccia, S. K. Kuiteing, A. Abrardo, et al. Progressive Compressed Sensing andReconstruction of Multidimensional Signals Using Hybrid Transform/Prediction SparsityModel[J]. IEEE Journal on Emerging and Selected Topics in Circuits and Systems.2012,2(3):340-352.
[46] E. J. Cand E S, J. Romberg. Practical Signal Recovery From Random Projections.[J]. Proc.SPIE Conf. Wavelet Applications in Signal and Image Processing XI.2005,5914.
[47] P. L. Combettes, H. J. Trussell. Method of Successive Projections for Finding a CommonPoint of Sets in Metric Spaces[J]. Journal Of Optimization Theory And Applications.1990,67(3):487-507.
[48] J. A. Tropp, A. C. Gilbert. Signal Recovery From Random Measurements Via OrthogonalMatching Pursuit[J]. IEEE Trans. on Information Theory.2007,53(12):4655-4666.
[49] C. La, M. N. Do. Signal Reconstruction Using Sparse Tree Representation[C].Proc.Wavelets XI at SPIE Optics and Photonics,2005.
[50] S. Sarvotham, D. Baron, R. G. Baraniuk. Fast Measurement and Reconstruction of SparseSignals[C].2006IEEE International Symposium on Information Theory,2006.
[51] S. Sarvotham, D. Baron, R. G. Baraniuk. Compressed Sensing Reconstruction Via BeliefPropagation[R].{},2006.
[52] E. J. Candes, T. Tao. Near-Optimal Signal Recovery From Random Projections:Universal Encoding Strategies?[J]. IEEE Trans. on Information Theory.2006,52(12):5406-5425.
[53] S. Chen, D. L. Donoho, M. A. Saunders. Atomic Decomposition by Basis Pursuit[J]. SiamJournal On Scientific Computing.1998,20(1):33-61.
[54] G. Peyre. Best Basis Compressed Sensing[J]. IEEE Trans. on Signal Processing.2010,58(5):2613-2622.
[55] M. Elad. Optimized Projections for Compressed Sensing[J]. IEEE Trans. on SignalProcessing.2007,55(12):5695-5702.
[56] S. Bhattacharya, T. Blumensath, B. Mulgrew, et al. Fast Encoding of Synthetic ApertureRadar Raw Data Using Compressed Sensing[C].14th Workshop on Statistical SignalProcessing, SSP '07. IEEE/SP,2007.
[57] J. N. Laska, S. Kirolos, M. F. Duarte, et al. Theory and Implementation of anAnalog-to-Information Converter Using Random Demodulation[C].IEEE InternationalSymposium on Circuits and Systems, ISCAS2007,2007.
[58] W. Bajwa, J. Haupt, A. Sayeed, et al. Compressive Wireless Sensing[C].The FifthInternational Conference on Information Processing in Sensor Networks, IPSN2006,2006.
[59] M. Lustig, D. L. Donoho, J. M. Pauly. Rapid Mr Imaging with Compressed Sensing andRandomly Under-Sampled3Dft Trajectories[C].Proc Ann Meeting of ISMRM, Seattle,WA, ISMRM2006,2006.
[60] W. Dai, M. A. Sheikh, O. Milenkovic, et al. Compressive Sensing Dna Microarrays[J].EURASIP J. Bioinformatics and Systems Biology.2009:1.
[61] P. Borgnat, P. Flandrin. Time-Frequency Localization From Sparsity Constraints[C].IEEEInternational Conference on Acoustics, Speech and Signal Processing, ICASSP2008,2008.
[62] J. W. Ma, F. X. Le Dimet. Deblurring From Highly Incomplete Measurements for RemoteSensing[J]. IEEE Trans. on Geoscience and Remote Sensing.2009,47(3):792-802.
[63] J. W. Ma. Single-Pixel Remote Sensing[J]. IEEE Geoscience and Remote Sensing Letters.2009,6(2):199-203.
[64] M. Jianwei. A Single-Pixel Imaging System for Remote Sensing by Two-Step IterativeCurvelet Thresholding[J]. IEEE Geoscience and Remote Sensing Letters.2009,6(4):676-680.
[65] S. Guangming, L. Jie, C. Xuyang, et al. Uwb Echo Signal Detection with Ultra-Low RateSampling Based On Compressed Sensing[J]. IEEE Trans. on Circuits and Systems II:Express Briefs.2008,55(4):379-383.
[66] L. Danhua, S. Guangming, G. Dahua. A New Method for Signal SparseDecomposition[C].International Symposium on Intelligent Signal Processing andCommunication Systems, ISPACS2007.,2007.
[67]刘丹华，石光明，周佳社等.基于Compressed Sensing框架的图像多描述编码方法[J].红外与毫米波学报.2009(04):298-302.
[68]刘丹华，石光明，周佳社.一种冗余字典下的信号稀疏分解新方法[J].西安电子科技大学学报.2008(02):228-232.
[69] L. Dan-Hua, S. Guang-Ming, G. Da-Hua, et al. A Robust Image Encryption Scheme OverWireless Channels[C].International Conference on Wireless Communications&SignalProcessing, WCSP2009,2009.
[70] S. Guang-Ming, G. Da-Hua, L. Dan-Hua, et al. High Resoluton Image Reconstruction: ANew Imager Via Movable Random Exposure[C].16th IEEE International Conference onImage Processing (ICIP),2009.
[71] S. Guangming, G. Dahua, S. Xiaoxia, et al. High-Resolution Imaging Via MovingRandom Exposure and its Simulation[J]. IEEE Trans. on Image Processing.2011,20(1):276-282.
[72]刘丹华，石光明，高大化等.基于原子库树状结构划分的诱导式信号稀疏分解[J].系统工程与电子技术.2009(08):1976-1981.
[73]石光明，刘丹华，高大化等.压缩感知理论及其研究进展[J].电子学报.2009(05):1070-1081.
[74] Nasa. Landsat7Science Data Users Handbook[R]. NASA(http://landsathandbook.gsfc.nasa.gov/),2011.
[75] R. O. Green, M. L. Eastwood, C. M. Sarture, et al. Imaging Spectroscopy and theAirborne Visible Infrared Imaging Spectrometer (Aviris)[J]. Remote Sensing OfEnvironment.1998,65(3):227-248.
[76] N. Gat. Imaging Spectroscopy Using Tunable Filters: A Review[C].SPIE-INT SOCOPTICAL ENGINEERING, BELLINGHAM: SPIE-INT SOC OPTICALENGINEERING,2000.
[77] A. Barducci, D. Guzzi, C. Lastri, et al. Theoretical Aspects of Fourier TransformSpectrometry and Common Path Triangular Interferometers[J]. Opt. Express.2010,18(11):11622-11649.
[78] L. W. Schumann, T. S. Lomheim. Infrared Hyperspectral Imaging Fourier Transform andDispersive Spectrometers: Comparison of Signal-to-Noise Based Performance[C].SPIE-INT SOC OPTICAL ENGINEERING, BELLINGHAM: SPIE-INT SOCOPTICAL ENGINEERING,2001.
[79]焦李成等.图像多尺度几何分析理论与应用[M].西安:西安电子科技大学出版社,2008.
[80]董伟生.基于上下文的自适应图像建模及其在图像恢复中的应用[D].西安电子科技大学,2011.
[81] R. L. Kashyap, R. Chellappa. Estimation and Choice of Neighbors in Spatial-InteractionModels of Images[J]. IEEE Trans. on Information Theory.1983,29(1):60-72.
[82] P. H. Westerink, J. Biemond, D. E. Boekee. An Optimal Bit Allocation Algorithm forSub-Band Coding[C].International Conference on Acoustics, Speech, and SignalProcessing, ICASSP-88,1988.
[83] J. Portilla, V. Strela, M. J. Wainwright, et al. Image Denoising Using Scale Mixtures ofGaussians in the Wavelet Domain[J]. IEEE Trans. on Image Processing.2003,12(11):1338-1351.
[84] L. Sendur, I. W. Selesnick. Bivariate Shrinkage Functions for Wavelet-Based DenoisingExploiting Interscale Dependency[J]. IEEE Trans. on Signal Processing.2002,50(11):2744-2756.
[85] S. Tan, L. C. Jiao. Multivariate Statistical Models for Image Denoising in the WaveletDomain[J]. International Journal Of Computer Vision.2007,75(2):209-230.
[86] Y. Hitomi, J. W. Gu, M. Gupta, et al. Video From a Single Coded Exposure PhotographUsing a Learned Over-Complete Dictionary[J].2011IEEE International Conference onComputer Vision (ICCV).2011:287-294.
[87] S. Farsiu, M. D. Robinson, M. Elad, et al. Fast and Robust Multiframe SuperResolution[J]. IEEE Trans. on Image Processing.2004,13(10):1327-1344.
[88] G. K. Chantas, N. P. Galatsanos, N. A. Woods. Super-Resolution Based On FastRegistration and Maximum a Posteriori Reconstruction[J]. IEEE Trans. on ImageProcessing.2007,16(7):1821-1830.
[89] M. Protter, M. Elad, H. Takeda, et al. Generalizing the Nonlocal-Means toSuper-Resolution Reconstruction[J]. IEEE Trans. on Image Processing.2009,18(1):36-51.
[90] E. Candes, J. Romberg. Signal Recovery From Random Projections[C].SPIE-INT SOCOPTICAL ENGINEERING, BELLINGHAM: SPIE-INT SOC OPTICALENGINEERING,2005.
[91] X. L. Wu, X. J. Zhang, J. Wang. Model-Guided Adaptive Recovery of CompressiveSensing[C].IEEE Data Compression Conference, LOS ALAMITOS: IEEE COMPUTERSOC,2008.
[92] R. Raskar, A. Agrawal, J. Tumblin. Coded Exposure Photography: Motion DeblurringUsing Fluttered Shutter[J]. Acm Transactions On Graphics.2006,25(3):795-804.
[93] S. C. Park, M. K. Park, M. G. Kang. Super-Resolution Image Reconstruction: ATechnical Overview[J]. IEEE Signal Processing Magazine.2003,20(3):21-36.
[94] High-Resolution Imaging Via Moving Random Exposure[Z], http://see.xidian.edu.cn/faculty/gmshi/web/index.htm.
[95] B. E. Bayer. Color Imaging Array[P]. U.S. Patent No.3971065. Jul.20,1976.
[96] D. Alleysson, S. Susstrunk, J. Herault. Linear Demosaicing Inspired by the Human VisualSystem[J]. IEEE Trans. on Image Processing.2005,14(4):439-449.
[97] W. Xiaolin, Z. Ning. Primary-Consistent Soft-Decision Color Demosaicking for DigitalCameras (Patent Pending)[J]. IEEE Trans. on Image Processing.2004,13(9):1263-1274.
[98] B. K. Gunturk, J. Glotzbach, Y. Altunbasak, et al. Demosaicking: Color Filter ArrayInterpolation[J]. IEEE Signal Processing Magazine.2005,22(1):44-54.
[99] K. Hirakawa, T. W. Parks. Adaptive Homogeneity-Directed Demosaicing Algorithm[J].IEEE Trans. on Image Processing.2005,14(3):360-369.
[100] P. Soo-Chang, T. Io-Kuong. Effective Color Interpolation in Ccd Color Filter ArraysUsing Signal Correlation[J]. IEEE Trans. on Circuits and Systems for Video Technology.2003,13(6):503-513.
[101] B. Tang, G. Sapiro, V. Caselles. Color Image Enhancement Via Chromaticity Diffusion[J].IEEE Trans. on Image Processing.2001,10(5):701-707.
[102] M. R. Gupta, T. Chen. Vector Color Filter Array Demosaicing[C].Proc. SPIE4306,Sensors and Camera Systems for Scientific, Industrial, and Digital PhotographyApplications II,2001.
[103] C. Lanlan, T. Yap-Peng. Effective Use of Spatial and Spectral Correlations for ColorFilter Array Demosaicking[J]. IEEE Trans. on Consumer Electronics.2004,50(1):355-365.
[104] L. Chang, Y. Tan. Hybrid Color Filter Array Demosaicking for Effective ArtifactSuppression[J]. Journal of Electronic Imaging.2006,15(01):13003.
[105] W. T. Freeman. Method and Apparatus for Reconstructing Missing Color Samples[P].U.S. Patent No.4774565.1988.
[106] B. K. Gunturk, Y. Altunbasak, R. M. Mersereau. Color Plane Interpolation UsingAlternating Projections[J]. IEEE Trans. on Image Processing.2002,11(9):997-1013.
[107] F. Zhang, X. L. Wu, X. K. Yang, et al. Robust Color Demosaicking with Adaptation toVarying Spectral Correlations[J]. IEEE Trans. on Image Processing.2009,18(12):2706-2717.
[108] B. Leung, G. Jeon, E. Dubois. Least-Squares Luma-Chroma Demultiplexing Algorithmfor Bayer Demosaicking[J]. IEEE Trans. on Image Processing.2011,20(7):1885-1894.
[109] H. S. Malvar, L. W. He, R. Cutler. High-Quality Linear Interpolation for Demosaicing ofBayer-Patterned Color Images[J].2004IEEE INTERNATIONAL CONFERENCE ONACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL III, PROCEEDINGS:IMAGE AND MULTIDIMENSIONAL SIGNAL PROCESSING SPECIAL SESSIONS.2004:485-488.
[110] S. Ferradans, M. Bertalmio, V. Caselles. Geometry-Based Demosaicking[J]. IEEE Trans.on Image Processing.2009,18(3):665-670.
[111] J. F. Hamilton, J. E. Adams. Adaptative Color Plan Interpolation in Single Sensor ColorElectronic Camera[P]. U.S. Patent No.5629734.1997.
[112] D. R. Cok. Signal Processing Method and Apparatus for Producing InterpolatedChrominance Values in a Sampled Color Image Signal[P]. U.S. patent No.4642678.1986.
[113] C. A. Laroche, M. A. Prescott. Apparatus and Method for Adaptively Interpolating a FullColor Image Utilizing Chrominance Gradients[P]. U.S. Patent No.5373322.1994.
[114] R. Kimmel. Demosaicing: Image Reconstruction From Color Ccd Samples[J]. IEEETrans. on Image Processing.1999,8(9):1221-1228.
[115] X. Li, B. Gunturk, L. Zhang. Image Demosaicing: A Systematic Survey[C].SPIE-INTSOC OPTICAL ENGINEERING, BELLINGHAM: SPIE-INT SOC OPTICALENGINEERING,2008.
[116] D. Menon. Color Image Reconstruction for Digital Cameras[D]. University of Padova,2008.
[117] X. Li, B. Gunturk, L. Zhang. Image Demosaicing: A Systematic Survey[C].SPIE-INTSOC OPTICAL ENGINEERING, BELLINGHAM: SPIE-INT SOC OPTICALENGINEERING,2008.
[118]施勇红.视频前端预处理中去马赛克技术研究[D].西安电子科技大学,2007.
[119]施勇红，霍俊彦，卢峰等.基于像素组的去马赛克算法[J].北京电子科技学院学报.2006(04):20-24.
[120]王涛.彩色图像混合去马赛克方法的研究[D].南京理工大学,2006.
[121]刘方.基于Bayer彩色滤波阵列插值算法的研究[D].电子科技大学,2006.
[122]贺钦，刘文予.数字图像传感器颜色插值算法研究[J].小型微型计算机系统.2007(08):1482-1485.
[123] S. G. Mallat, Z. Zhang. Matching Pursuits with Time-Frequency Dictionaries[J]. IEEETrans. on Signal Processing.1993,41(12):3397-3415.
[124] E. J. Candès. Ridgelets: Theory and Applications[D]. Stanford University, USA,1998.
[125] E. J. Candès. Monoscale Ridgelets for the Representation of Images with Edges[R].Department of Statistics, Stanford University, USA,1999.
[126] E. J. Candès, D. L. Donoho. Curvelets[R]. Department of Statistics, Stanford University,USA,1999.
[127] E. Le Pennec, S. Mallat. Image Compression with Geometrical Wavelets[C].ImageProcessing,2000. Proceedings.2000International Conference on,2000.
[128]焦李成，谭山.图像的多尺度几何分析:回顾和展望[J].电子学报.2003(S1):1975-1981.
[129] X. Peyre, G. Best Basis Compressed Sensing[J]. IEEE Trans. on Signal Processing.2010,58(5):2613-2622.
[130] E. Angel. Interactive Computer Graphics: A Top-Down Approach Using Opengl[M].Addison-Wesley,2003.
[131] P. Comninos. Mathematical and Computer Programming Techniques for ComputerGraphics[M]. Springer,2005.
[132] J. D. Foley, A. V. Dam, S. K. Feiner, et al. Introduction to Computer Graphics[M].Addison-Wesley.
[133] K. Fukunaga. Introduction to Statistical Pattern Recognition[M].2nd Revised edition ed.Academic Press Inc.
[134] W. T. Yin, S. Osher, D. Goldfarb, et al. Bregman Iterative Algorithms forL(1)-Minimization with Applications to Compressed Sensing[J]. SIAM Journal onImaging Sciences.2008,1(1):143-168.
[135] P. L. Combettes, V. R. Wajs. Signal Recovery by Proximal Forward-BackwardSplitting[J]. Multiscale Modeling&Simulation.2005,4(4):1168-1200.
[136] A. Beck, M. Teboulle. A Fast Iterative Shrinkage-Thresholding Algorithm for LinearInverse Problems[J]. Siam Journal on Imaging Sciences.2009,2(1):183-202.
[137] L. Wenmiao, T. Yap-Peng. Color Filter Array Demosaicking: New Method andPerformance Measures[J]. IEEE Trans. on Image Processing.2003,12(10):1194-1210.
[138] V. P. Shah, N. H. Younan, R. L. King. An Efficient Pan-Sharpening Method Via aCombined Adaptive Pca Approach and Contourlets[J]. IEEE Trans. on Geoscience andRemote Sensing.2008,46(5):1323-1335.
[139] A. Garzelli, F. Nencini, L. Alparone, et al. Pan-Sharpening of Multispectral Images: ACritical Review and Comparison[C].2004IEEE International Geoscience and RemoteSensing Symposium,2004. IGARSS '04,2004.
[140] C. A. Laben, B. V. Brower. Process for Enhancing the Spatial Resolution of MultispectralImagery Using Pan-Sharpening[P]. US Patent No.6011875.2000.
[141] A. Levin, R. Fergus, F. Durand, et al. Image and Depth From a Conventional Camera witha Coded Aperture[J]. Acm Transactions On Graphics.2007,26(703).
[142] S. R. Gottesman, E. E. Fenimore. New Family of Binary Arrays for Coded ApertureImaging.[J]. Applied Optics.1989,28(20).
[143] R. F. Marcia, R. M. Willett. Compressive Coded Aperture Superresolution ImageReconstruction[C].IEEE International Conference on Acoustics, Speech and SignalProcessing, ICASSP2008,2008.
[144] R. M. Willett, M. E. Gehm, D. J. Brady. Multiscale Reconstruction for ComputationalSpectral Imaging-Art. No.64980L[C].SPIE-INT SOC OPTICAL ENGINEERING,BELLINGHAM: SPIE-INT SOC OPTICAL ENGINEERING,2007.
[145] J. A. Tropp, J. N. Laska, M. F. Duarte, et al. Beyond Nyquist: Efficient Sampling ofSparse Bandlimited Signals[J]. IEEE Trans. on Information Theory.2010,56(1):520-544.
[2] E. J. Candès. Compressive Sampling[C].Proceedings of the International Congress ofMathematicians, Madrid, Spain:2006.
[3] E. J. Candes, J. Romberg, T. Tao. Robust Uncertainty Principles: Exact SignalReconstruction From Highly Incomplete Frequency Information[J]. IEEE Trans. onInformation Theory.2006,52(2):489-509.
[4] E. J. Candes, J. Romberg. Quantitative Robust Uncertainty Principles and OptimallySparse Decompositions[J]. Foundations Of Computational Mathematics.2006,6(2):227-254.
[5] D. L. Donoho. Compressed Sensing[J]. IEEE Trans. on Information Theory.2006,52(4):1289-1306.
[6] Y. Tsaig, D. L. Donoho. Extensions of Compressed Sensing[J]. Signal Processing.2006,86(3):549-571.
[7] F. Durand, R. Szeliski. Computational Photography[J]. IEEE Comput Graph Appl.2007,27(2):21-22.
[8] R. Raskar. Less is More: Coded Computational Photography[M]. BERLIN:SPRINGER-VERLAG BERLIN,2007:4843,1-12.
[9] O. Bimber. Computational Photography-The Next Big Step[J]. Computer.2006,39(8):28-29.
[10] D. J. Brady. Optical Imaging and Spectroscopy[M]. Wiley-OSA,2009.
[11] J. L. Suo, X. Y. Ji, Q. H. Dai. An Overview of Computational Photography[J]. ScienceChina-Information Sciences.2012,55(6):1229-1248.
[12]徐树奎，涂丹，李国辉等.计算摄影综述[J].计算机应用研究.2010(11):4032-4039.
[13] J. Romberg. Imaging Via Compressive Sampling[J]. IEEE Signal Processing Magazine.2008,25(2):14-20.
[14] J. Romberg. Compressive Sensing by Random Convolution[J]. SIAM J. Img. Sci.2009,2(4):1098-1128.
[15] M. F. Duarte, M. A. Davenport, D. Takhar, et al. Single-Pixel Imaging Via CompressiveSampling[J]. IEEE Signal Processing Magazine.2008,25(2):83-91.
[16] M. B. Wakin, J. N. Laska, M. F. Duarte, et al. An Architecture for CompressiveImaging[C].2006IEEE International Conference on Image Processing,2006.
[17] D. Takhar, J. N. Laska, M. B. Wakin, et al. A New Compressive Imaging CameraArchitecture Using Optical-Domain Compression[J]. Computational Imaging IV atIS&T/SPIE Electronic Imaging.2006:43-52.
[18] C. Hongzhi, X. Ning, S. Bo, et al. Infrared Camera Using a Single Nano-Photodetector[J].IEEE Sensors Journal.2013,13(3):949-958.
[19] A. C. Fannjiang. Exact Localization and Superresolution with Noisy Data and RandomIllumination[J]. Inverse Problems.2013,2011(27):65012.
[20] A. Agrawal, R. Raskar. Resolving Objects at Higher Resolution From a SingleMotion-Blurred Image[C].IEEE Conference on Computer Vision and Pattern Recognition,CVPR '07.,2007.
[21] P. Nagesh, L. Baoxin. Compressive Imaging of Color Images[C].IEEE InternationalConference on Acoustics, Speech and Signal Processing, ICASSP2009,2009.
[22] B. Khademhosseinieh, G. Biener, I. Sencan, et al. Lensfree Color Imaging On aNanostructured Chip Using Compressive Decoding[J]. Applied Physics Letters.2010,97(21):211112.
[23] B. Khademhosseinieh, G. Biener, I. Sencan, et al. Lensless On-Chip Color Imaging UsingNano-Structured Surfaces and Compressive Decoding[C].2011Conference on Lasers andElectro-Optics (CLEO),2011.
[24] A. F. Coskun, T. Su, I. Sencan, et al. Lensfree Fluorescent On-Chip ImagingusingCompressive Sampling[J]. Opt. Photon. News.2010,21(12):27.
[25] A. Majumdar, R. K. Ward, T. Aboulnasr. Algorithms to Approximately Solve Np HardRow-Sparse Mmv Recovery Problem: Application to Compressive Color Imaging[J].IEEE Journal on Emerging and Selected Topics in Circuits and Systems.2012,2(3):362-369.
[26] A. Majumdar, R. K. Ward. Compressive Color Imaging with Group-Sparsity On AnalysisPrior[C].201017th IEEE International Conference on Image Processing (ICIP),2010.
[27] A. Wagadarikar, R. John, R. Willett, et al. Single Disperser Design for Coded ApertureSnapshot Spectral Imaging[J]. Applied Optics.2008,47(10): B44-B51.
[28] M. E. Gehm, R. John, D. J. Brady, et al. Single-Shot Compressive Spectral Imaging witha Dual-Disperser Architecture[J]. Opt. Express.2007,15(21):14013-14027.
[29] P. Ye, H. Arguello, G. Arce. Spectral Aperture Code Design for Multi-Shot CompressiveSpectral Imaging[C].Optical Society of America,2010.
[30] A. Mrozack, D. L. Marks, D. J. Brady. Coded Aperture Spectroscopy with Denoisingthrough Sparsity[J]. Opt. Express.2012,20(3):2297-2309.
[31] R. M. Willett, M. E. Gehm, D. J. Brady. Multiscale Reconstruction for ComputationalSpectral Imaging [C].SPIE-INT SOC OPTICAL ENGINEERING, BELLINGHAM:SPIE-INT SOC OPTICAL ENGINEERING,2007.
[32] A. A. Wagadarikar, N. P. Pitsianis, X. Sun, et al. Video Rate Spectral Imaging Using aCoded Aperture Snapshot Spectral Imager[J]. Optics Express.2009,17(8):6368-6388.
[33] S. Xianbiao, N. Ahuja. Imaging Via Three-Dimensional Compressive Sampling(3Dcs)[C].2011IEEE International Conference on Computer Vision (ICCV),2011.
[34] A. C. Sankaranarayanan, P. K. Turaga, R. G. Baraniuk, et al. Compressive Acquisition ofDynamic Scenes[C].ECCV'10, Berlin, Heidelberg: Springer-Verlag,2010.
[35] Y. Hitomi, G. Jinwei, M. Gupta, et al. Video From a Single Coded Exposure PhotographUsing a Learned Over-Complete Dictionary[C].2011IEEE International Conference onComputer Vision (ICCV),2011.
[36] R. F. Marcia, R. M. Willett. Compressive Coded Aperture Superresolution ImageReconstruction[C].IEEE International Conference on Acoustics, Speech and SignalProcessing, ICASSP2008,2008.
[37] X. Jie, M. Jianwei, Z. Dongming, et al. Compressive Video Sensing Based On UserAttention Model[C].Picture Coding Symposium (PCS2010),2010.
[38] Y. P. Jae, M. B. Wakin. A Multiscale Framework for Compressive Sensing ofVideo[C].Picture Coding Symposium, PCS2009,2009.
[39] M. B. Wakin, J. N. Laska, M. F. Duarte, et al. Compressive Imaging for VideoRepresentation and Coding[C].Proceedings of Picture Coding Symposium (PCS2006),2006.
[40] X. Siyuan, C. Lin. Scalable Video Coding with Compressive Sensing for WirelessVideocast[C].2011IEEE International Conference on Communications (ICC),2011.
[41] S. Pudlewski, A. Prasanna, T. Melodia. Compressed-Sensing-Enabled Video Streamingfor Wireless Multimedia Sensor Networks[J]. IEEE Trans. on Mobile Computing.2012,11(6):1060-1072.
[42] S. Pudlewski, T. Melodia. A Rate-Energy-Distortion Analysis for Compressed-Sensing-Enabled Wireless Video Streaming On Multimedia Sensors[C].2011IEEE GlobalTelecommunications Conference (GLOBECOM2011),2011.
[43] L. Sungwon, A. Ortega. Adaptive Compressed Sensing for Depthmap Compression UsingGraph-Based Transform[C].201219th IEEE International Conference on ImageProcessing (ICIP),2012.
[44] G. Coluccia, E. Magli. A Novel Progressive Image Scanning and Reconstruction SchemeBased On Compressed Sensing and Linear Prediction[C].2012IEEE InternationalConference on Multimedia and Expo (ICME),2012.
[45] G. Coluccia, S. K. Kuiteing, A. Abrardo, et al. Progressive Compressed Sensing andReconstruction of Multidimensional Signals Using Hybrid Transform/Prediction SparsityModel[J]. IEEE Journal on Emerging and Selected Topics in Circuits and Systems.2012,2(3):340-352.
[46] E. J. Cand E S, J. Romberg. Practical Signal Recovery From Random Projections.[J]. Proc.SPIE Conf. Wavelet Applications in Signal and Image Processing XI.2005,5914.
[47] P. L. Combettes, H. J. Trussell. Method of Successive Projections for Finding a CommonPoint of Sets in Metric Spaces[J]. Journal Of Optimization Theory And Applications.1990,67(3):487-507.
[48] J. A. Tropp, A. C. Gilbert. Signal Recovery From Random Measurements Via OrthogonalMatching Pursuit[J]. IEEE Trans. on Information Theory.2007,53(12):4655-4666.
[49] C. La, M. N. Do. Signal Reconstruction Using Sparse Tree Representation[C].Proc.Wavelets XI at SPIE Optics and Photonics,2005.
[50] S. Sarvotham, D. Baron, R. G. Baraniuk. Fast Measurement and Reconstruction of SparseSignals[C].2006IEEE International Symposium on Information Theory,2006.
[51] S. Sarvotham, D. Baron, R. G. Baraniuk. Compressed Sensing Reconstruction Via BeliefPropagation[R].{},2006.
[52] E. J. Candes, T. Tao. Near-Optimal Signal Recovery From Random Projections:Universal Encoding Strategies?[J]. IEEE Trans. on Information Theory.2006,52(12):5406-5425.
[53] S. Chen, D. L. Donoho, M. A. Saunders. Atomic Decomposition by Basis Pursuit[J]. SiamJournal On Scientific Computing.1998,20(1):33-61.
[54] G. Peyre. Best Basis Compressed Sensing[J]. IEEE Trans. on Signal Processing.2010,58(5):2613-2622.
[55] M. Elad. Optimized Projections for Compressed Sensing[J]. IEEE Trans. on SignalProcessing.2007,55(12):5695-5702.
[56] S. Bhattacharya, T. Blumensath, B. Mulgrew, et al. Fast Encoding of Synthetic ApertureRadar Raw Data Using Compressed Sensing[C].14th Workshop on Statistical SignalProcessing, SSP '07. IEEE/SP,2007.
[57] J. N. Laska, S. Kirolos, M. F. Duarte, et al. Theory and Implementation of anAnalog-to-Information Converter Using Random Demodulation[C].IEEE InternationalSymposium on Circuits and Systems, ISCAS2007,2007.
[58] W. Bajwa, J. Haupt, A. Sayeed, et al. Compressive Wireless Sensing[C].The FifthInternational Conference on Information Processing in Sensor Networks, IPSN2006,2006.
[59] M. Lustig, D. L. Donoho, J. M. Pauly. Rapid Mr Imaging with Compressed Sensing andRandomly Under-Sampled3Dft Trajectories[C].Proc Ann Meeting of ISMRM, Seattle,WA, ISMRM2006,2006.
[60] W. Dai, M. A. Sheikh, O. Milenkovic, et al. Compressive Sensing Dna Microarrays[J].EURASIP J. Bioinformatics and Systems Biology.2009:1.
[61] P. Borgnat, P. Flandrin. Time-Frequency Localization From Sparsity Constraints[C].IEEEInternational Conference on Acoustics, Speech and Signal Processing, ICASSP2008,2008.
[62] J. W. Ma, F. X. Le Dimet. Deblurring From Highly Incomplete Measurements for RemoteSensing[J]. IEEE Trans. on Geoscience and Remote Sensing.2009,47(3):792-802.
[63] J. W. Ma. Single-Pixel Remote Sensing[J]. IEEE Geoscience and Remote Sensing Letters.2009,6(2):199-203.
[64] M. Jianwei. A Single-Pixel Imaging System for Remote Sensing by Two-Step IterativeCurvelet Thresholding[J]. IEEE Geoscience and Remote Sensing Letters.2009,6(4):676-680.
[65] S. Guangming, L. Jie, C. Xuyang, et al. Uwb Echo Signal Detection with Ultra-Low RateSampling Based On Compressed Sensing[J]. IEEE Trans. on Circuits and Systems II:Express Briefs.2008,55(4):379-383.
[66] L. Danhua, S. Guangming, G. Dahua. A New Method for Signal SparseDecomposition[C].International Symposium on Intelligent Signal Processing andCommunication Systems, ISPACS2007.,2007.
[67]刘丹华，石光明，周佳社等.基于Compressed Sensing框架的图像多描述编码方法[J].红外与毫米波学报.2009(04):298-302.
[68]刘丹华，石光明，周佳社.一种冗余字典下的信号稀疏分解新方法[J].西安电子科技大学学报.2008(02):228-232.
[69] L. Dan-Hua, S. Guang-Ming, G. Da-Hua, et al. A Robust Image Encryption Scheme OverWireless Channels[C].International Conference on Wireless Communications&SignalProcessing, WCSP2009,2009.
[70] S. Guang-Ming, G. Da-Hua, L. Dan-Hua, et al. High Resoluton Image Reconstruction: ANew Imager Via Movable Random Exposure[C].16th IEEE International Conference onImage Processing (ICIP),2009.
[71] S. Guangming, G. Dahua, S. Xiaoxia, et al. High-Resolution Imaging Via MovingRandom Exposure and its Simulation[J]. IEEE Trans. on Image Processing.2011,20(1):276-282.
[72]刘丹华，石光明，高大化等.基于原子库树状结构划分的诱导式信号稀疏分解[J].系统工程与电子技术.2009(08):1976-1981.
[73]石光明，刘丹华，高大化等.压缩感知理论及其研究进展[J].电子学报.2009(05):1070-1081.
[74] Nasa. Landsat7Science Data Users Handbook[R]. NASA(http://landsathandbook.gsfc.nasa.gov/),2011.
[75] R. O. Green, M. L. Eastwood, C. M. Sarture, et al. Imaging Spectroscopy and theAirborne Visible Infrared Imaging Spectrometer (Aviris)[J]. Remote Sensing OfEnvironment.1998,65(3):227-248.
[76] N. Gat. Imaging Spectroscopy Using Tunable Filters: A Review[C].SPIE-INT SOCOPTICAL ENGINEERING, BELLINGHAM: SPIE-INT SOC OPTICALENGINEERING,2000.
[77] A. Barducci, D. Guzzi, C. Lastri, et al. Theoretical Aspects of Fourier TransformSpectrometry and Common Path Triangular Interferometers[J]. Opt. Express.2010,18(11):11622-11649.
[78] L. W. Schumann, T. S. Lomheim. Infrared Hyperspectral Imaging Fourier Transform andDispersive Spectrometers: Comparison of Signal-to-Noise Based Performance[C].SPIE-INT SOC OPTICAL ENGINEERING, BELLINGHAM: SPIE-INT SOCOPTICAL ENGINEERING,2001.
[79]焦李成等.图像多尺度几何分析理论与应用[M].西安:西安电子科技大学出版社,2008.
[80]董伟生.基于上下文的自适应图像建模及其在图像恢复中的应用[D].西安电子科技大学,2011.
[81] R. L. Kashyap, R. Chellappa. Estimation and Choice of Neighbors in Spatial-InteractionModels of Images[J]. IEEE Trans. on Information Theory.1983,29(1):60-72.
[82] P. H. Westerink, J. Biemond, D. E. Boekee. An Optimal Bit Allocation Algorithm forSub-Band Coding[C].International Conference on Acoustics, Speech, and SignalProcessing, ICASSP-88,1988.
[83] J. Portilla, V. Strela, M. J. Wainwright, et al. Image Denoising Using Scale Mixtures ofGaussians in the Wavelet Domain[J]. IEEE Trans. on Image Processing.2003,12(11):1338-1351.
[84] L. Sendur, I. W. Selesnick. Bivariate Shrinkage Functions for Wavelet-Based DenoisingExploiting Interscale Dependency[J]. IEEE Trans. on Signal Processing.2002,50(11):2744-2756.
[85] S. Tan, L. C. Jiao. Multivariate Statistical Models for Image Denoising in the WaveletDomain[J]. International Journal Of Computer Vision.2007,75(2):209-230.
[86] Y. Hitomi, J. W. Gu, M. Gupta, et al. Video From a Single Coded Exposure PhotographUsing a Learned Over-Complete Dictionary[J].2011IEEE International Conference onComputer Vision (ICCV).2011:287-294.
[87] S. Farsiu, M. D. Robinson, M. Elad, et al. Fast and Robust Multiframe SuperResolution[J]. IEEE Trans. on Image Processing.2004,13(10):1327-1344.
[88] G. K. Chantas, N. P. Galatsanos, N. A. Woods. Super-Resolution Based On FastRegistration and Maximum a Posteriori Reconstruction[J]. IEEE Trans. on ImageProcessing.2007,16(7):1821-1830.
[89] M. Protter, M. Elad, H. Takeda, et al. Generalizing the Nonlocal-Means toSuper-Resolution Reconstruction[J]. IEEE Trans. on Image Processing.2009,18(1):36-51.
[90] E. Candes, J. Romberg. Signal Recovery From Random Projections[C].SPIE-INT SOCOPTICAL ENGINEERING, BELLINGHAM: SPIE-INT SOC OPTICALENGINEERING,2005.
[91] X. L. Wu, X. J. Zhang, J. Wang. Model-Guided Adaptive Recovery of CompressiveSensing[C].IEEE Data Compression Conference, LOS ALAMITOS: IEEE COMPUTERSOC,2008.
[92] R. Raskar, A. Agrawal, J. Tumblin. Coded Exposure Photography: Motion DeblurringUsing Fluttered Shutter[J]. Acm Transactions On Graphics.2006,25(3):795-804.
[93] S. C. Park, M. K. Park, M. G. Kang. Super-Resolution Image Reconstruction: ATechnical Overview[J]. IEEE Signal Processing Magazine.2003,20(3):21-36.
[94] High-Resolution Imaging Via Moving Random Exposure[Z], http://see.xidian.edu.cn/faculty/gmshi/web/index.htm.
[95] B. E. Bayer. Color Imaging Array[P]. U.S. Patent No.3971065. Jul.20,1976.
[96] D. Alleysson, S. Susstrunk, J. Herault. Linear Demosaicing Inspired by the Human VisualSystem[J]. IEEE Trans. on Image Processing.2005,14(4):439-449.
[97] W. Xiaolin, Z. Ning. Primary-Consistent Soft-Decision Color Demosaicking for DigitalCameras (Patent Pending)[J]. IEEE Trans. on Image Processing.2004,13(9):1263-1274.
[98] B. K. Gunturk, J. Glotzbach, Y. Altunbasak, et al. Demosaicking: Color Filter ArrayInterpolation[J]. IEEE Signal Processing Magazine.2005,22(1):44-54.
[99] K. Hirakawa, T. W. Parks. Adaptive Homogeneity-Directed Demosaicing Algorithm[J].IEEE Trans. on Image Processing.2005,14(3):360-369.
[100] P. Soo-Chang, T. Io-Kuong. Effective Color Interpolation in Ccd Color Filter ArraysUsing Signal Correlation[J]. IEEE Trans. on Circuits and Systems for Video Technology.2003,13(6):503-513.
[101] B. Tang, G. Sapiro, V. Caselles. Color Image Enhancement Via Chromaticity Diffusion[J].IEEE Trans. on Image Processing.2001,10(5):701-707.
[102] M. R. Gupta, T. Chen. Vector Color Filter Array Demosaicing[C].Proc. SPIE4306,Sensors and Camera Systems for Scientific, Industrial, and Digital PhotographyApplications II,2001.
[103] C. Lanlan, T. Yap-Peng. Effective Use of Spatial and Spectral Correlations for ColorFilter Array Demosaicking[J]. IEEE Trans. on Consumer Electronics.2004,50(1):355-365.
[104] L. Chang, Y. Tan. Hybrid Color Filter Array Demosaicking for Effective ArtifactSuppression[J]. Journal of Electronic Imaging.2006,15(01):13003.
[105] W. T. Freeman. Method and Apparatus for Reconstructing Missing Color Samples[P].U.S. Patent No.4774565.1988.
[106] B. K. Gunturk, Y. Altunbasak, R. M. Mersereau. Color Plane Interpolation UsingAlternating Projections[J]. IEEE Trans. on Image Processing.2002,11(9):997-1013.
[107] F. Zhang, X. L. Wu, X. K. Yang, et al. Robust Color Demosaicking with Adaptation toVarying Spectral Correlations[J]. IEEE Trans. on Image Processing.2009,18(12):2706-2717.
[108] B. Leung, G. Jeon, E. Dubois. Least-Squares Luma-Chroma Demultiplexing Algorithmfor Bayer Demosaicking[J]. IEEE Trans. on Image Processing.2011,20(7):1885-1894.
[109] H. S. Malvar, L. W. He, R. Cutler. High-Quality Linear Interpolation for Demosaicing ofBayer-Patterned Color Images[J].2004IEEE INTERNATIONAL CONFERENCE ONACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL III, PROCEEDINGS:IMAGE AND MULTIDIMENSIONAL SIGNAL PROCESSING SPECIAL SESSIONS.2004:485-488.
[110] S. Ferradans, M. Bertalmio, V. Caselles. Geometry-Based Demosaicking[J]. IEEE Trans.on Image Processing.2009,18(3):665-670.
[111] J. F. Hamilton, J. E. Adams. Adaptative Color Plan Interpolation in Single Sensor ColorElectronic Camera[P]. U.S. Patent No.5629734.1997.
[112] D. R. Cok. Signal Processing Method and Apparatus for Producing InterpolatedChrominance Values in a Sampled Color Image Signal[P]. U.S. patent No.4642678.1986.
[113] C. A. Laroche, M. A. Prescott. Apparatus and Method for Adaptively Interpolating a FullColor Image Utilizing Chrominance Gradients[P]. U.S. Patent No.5373322.1994.
[114] R. Kimmel. Demosaicing: Image Reconstruction From Color Ccd Samples[J]. IEEETrans. on Image Processing.1999,8(9):1221-1228.
[115] X. Li, B. Gunturk, L. Zhang. Image Demosaicing: A Systematic Survey[C].SPIE-INTSOC OPTICAL ENGINEERING, BELLINGHAM: SPIE-INT SOC OPTICALENGINEERING,2008.
[116] D. Menon. Color Image Reconstruction for Digital Cameras[D]. University of Padova,2008.
[117] X. Li, B. Gunturk, L. Zhang. Image Demosaicing: A Systematic Survey[C].SPIE-INTSOC OPTICAL ENGINEERING, BELLINGHAM: SPIE-INT SOC OPTICALENGINEERING,2008.
[118]施勇红.视频前端预处理中去马赛克技术研究[D].西安电子科技大学,2007.
[119]施勇红，霍俊彦，卢峰等.基于像素组的去马赛克算法[J].北京电子科技学院学报.2006(04):20-24.
[120]王涛.彩色图像混合去马赛克方法的研究[D].南京理工大学,2006.
[121]刘方.基于Bayer彩色滤波阵列插值算法的研究[D].电子科技大学,2006.
[122]贺钦，刘文予.数字图像传感器颜色插值算法研究[J].小型微型计算机系统.2007(08):1482-1485.
[123] S. G. Mallat, Z. Zhang. Matching Pursuits with Time-Frequency Dictionaries[J]. IEEETrans. on Signal Processing.1993,41(12):3397-3415.
[124] E. J. Candès. Ridgelets: Theory and Applications[D]. Stanford University, USA,1998.
[125] E. J. Candès. Monoscale Ridgelets for the Representation of Images with Edges[R].Department of Statistics, Stanford University, USA,1999.
[126] E. J. Candès, D. L. Donoho. Curvelets[R]. Department of Statistics, Stanford University,USA,1999.
[127] E. Le Pennec, S. Mallat. Image Compression with Geometrical Wavelets[C].ImageProcessing,2000. Proceedings.2000International Conference on,2000.
[128]焦李成，谭山.图像的多尺度几何分析:回顾和展望[J].电子学报.2003(S1):1975-1981.
[129] X. Peyre, G. Best Basis Compressed Sensing[J]. IEEE Trans. on Signal Processing.2010,58(5):2613-2622.
[130] E. Angel. Interactive Computer Graphics: A Top-Down Approach Using Opengl[M].Addison-Wesley,2003.
[131] P. Comninos. Mathematical and Computer Programming Techniques for ComputerGraphics[M]. Springer,2005.
[132] J. D. Foley, A. V. Dam, S. K. Feiner, et al. Introduction to Computer Graphics[M].Addison-Wesley.
[133] K. Fukunaga. Introduction to Statistical Pattern Recognition[M].2nd Revised edition ed.Academic Press Inc.
[134] W. T. Yin, S. Osher, D. Goldfarb, et al. Bregman Iterative Algorithms forL(1)-Minimization with Applications to Compressed Sensing[J]. SIAM Journal onImaging Sciences.2008,1(1):143-168.
[135] P. L. Combettes, V. R. Wajs. Signal Recovery by Proximal Forward-BackwardSplitting[J]. Multiscale Modeling&Simulation.2005,4(4):1168-1200.
[136] A. Beck, M. Teboulle. A Fast Iterative Shrinkage-Thresholding Algorithm for LinearInverse Problems[J]. Siam Journal on Imaging Sciences.2009,2(1):183-202.
[137] L. Wenmiao, T. Yap-Peng. Color Filter Array Demosaicking: New Method andPerformance Measures[J]. IEEE Trans. on Image Processing.2003,12(10):1194-1210.
[138] V. P. Shah, N. H. Younan, R. L. King. An Efficient Pan-Sharpening Method Via aCombined Adaptive Pca Approach and Contourlets[J]. IEEE Trans. on Geoscience andRemote Sensing.2008,46(5):1323-1335.
[139] A. Garzelli, F. Nencini, L. Alparone, et al. Pan-Sharpening of Multispectral Images: ACritical Review and Comparison[C].2004IEEE International Geoscience and RemoteSensing Symposium,2004. IGARSS '04,2004.
[140] C. A. Laben, B. V. Brower. Process for Enhancing the Spatial Resolution of MultispectralImagery Using Pan-Sharpening[P]. US Patent No.6011875.2000.
[141] A. Levin, R. Fergus, F. Durand, et al. Image and Depth From a Conventional Camera witha Coded Aperture[J]. Acm Transactions On Graphics.2007,26(703).
[142] S. R. Gottesman, E. E. Fenimore. New Family of Binary Arrays for Coded ApertureImaging.[J]. Applied Optics.1989,28(20).
[143] R. F. Marcia, R. M. Willett. Compressive Coded Aperture Superresolution ImageReconstruction[C].IEEE International Conference on Acoustics, Speech and SignalProcessing, ICASSP2008,2008.
[144] R. M. Willett, M. E. Gehm, D. J. Brady. Multiscale Reconstruction for ComputationalSpectral Imaging-Art. No.64980L[C].SPIE-INT SOC OPTICAL ENGINEERING,BELLINGHAM: SPIE-INT SOC OPTICAL ENGINEERING,2007.
[145] J. A. Tropp, J. N. Laska, M. F. Duarte, et al. Beyond Nyquist: Efficient Sampling ofSparse Bandlimited Signals[J]. IEEE Trans. on Information Theory.2010,56(1):520-544.