光学学报, 2020, 40 (1): 0111006, 网络出版: 2020-01-06   

压缩感知在光学成像领域的应用 下载: 5749次特邀综述

Applications of Compressive Sensing in Optical Imaging
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北京理工大学光电学院, 北京 100081
引用该论文

柯钧, 张临夏, 周群. 压缩感知在光学成像领域的应用[J]. 光学学报, 2020, 40(1): 0111006.

Jun Ke, Linxia Zhang, Qun Zhou. Applications of Compressive Sensing in Optical Imaging[J]. Acta Optica Sinica, 2020, 40(1): 0111006.

参考文献

[1] Neifeld M A, Shankar P. Feature-specific imaging[J]. Applied Optics, 2003, 42(17): 3379-3389.

[2] Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.

[3] Baraniuk R G. Compressive sensing [lecture notes][J]. IEEE Signal Processing Magazine, 2007, 24(4): 118-121.

[4] Kutyniok G. Compressed sensing: theory and applications[J]. Corr, 2012, 52(4): 1289-1306.

[5] Antonini M, Barlaud M, Mathieu P, et al. Image coding using wavelet transform[J]. IEEE Transactions on Image Processing, 1992, 1(2): 205-220.

[6] Daubechies I. The wavelet transform, time-frequency localization and signal analysis[J]. IEEE Transactions on Information Theory, 1990, 36(5): 961-1005.

[7] Starck J L, Candès E J, Donoho D L. The curvelet transform for image denoising[J]. IEEE Transactions on Image Processing, 2002, 11(6): 670-684.

[8] Candès E, Demanet L, Donoho D, et al. Fast discrete curvelet transforms[J]. Multiscale Modeling & Simulation, 2006, 5(3): 861-899.

[9] SternA. Optical compressive imaging[M]. Boca Raton: CRC Press, 2016.

[10] Marques E C, Maciel N, Naviner L, et al. A review of sparse recovery algorithms[J]. IEEE Access, 2018, 7: 1300-1322.

[11] DraganicA, OrovicI, Stankovic S. On some common compressive sensing recovery algorithms and applications-reviewpaper[J/OL]. ( 2017-04-21)[2019-09-08]. top/abs/1705. 05216. https://arxiv.xilesou.

[12] Vaswani N, Zhan J C. Recursive recovery of sparse signal sequences from compressive measurements: a review[J]. IEEE Transactions on Signal Processing, 2016, 64(13): 3523-3549.

[13] Duarte M F, Davenport M A, Takhar D, et al. Single-pixel imaging via compressive sampling[J]. IEEE Signal Processing Magazine, 2008, 25(2): 83-91.

[14] Gehm M E, Brady D J. Compressive sensing in the EO/IR[J]. Applied Optics, 2015, 54(8): C14-C22.

[15] Gerrits T, Lum D J, Verma V, et al. Short-wave infrared compressive imaging of single photons[J]. Optics Express, 2018, 26(12): 15519-15527.

[16] Ke J, Ashok A, Neifeld M A. Block-wise motion detection using compressive imaging system[J]. Optics Communications, 2011, 284(5): 1170-1180.

[17] GoodfellowI, BengioY, CourvilleA. Deep learning[M]. USA: MIT Press, 2016.

[18] LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.

[19] Candès E J. The restricted isometry property and its implications for compressed sensing[J]. Comptes Rendus Mathematique, 2008, 346(9/10): 589-592.

[20] Elad M. Optimized projections for compressed sensing[J]. IEEE Transactions on Signal Processing, 2007, 55(12): 5695-5702.

[21] Duarte-Carvajalino J M, Sapiro G. Learning to sense sparse signals: simultaneous sensing matrix and sparsifying dictionary optimization[J]. IEEE Transactions on Image Processing, 2009, 18(7): 1395-1408.

[22] Xu J P, Pi Y M, Cao Z J. Optimized projection matrix for compressive sensing[J]. EURASIP Journal on Advances in Signal Processing, 2010, 2010: 560349.

[23] Lu C, Li H, Lin Z. Optimized projections for compressed sensing via direct mutual coherence minimization[J]. Signal Processing, 2018, 151: 45-55.

[24] 王强, 张培林, 王怀光, 等. 压缩感知中测量矩阵构造综述[J]. 计算机应用, 2017, 37(1): 188-196.

    Wang Q, Zhang P L, Wang H G, et al. Survey on construction of measurement matrices in compressive sensing[J]. Journal of Computer Applications, 2017, 37(1): 188-196.

[25] 王强, 李佳, 沈毅. 压缩感知中确定性测量矩阵构造算法综述[J]. 电子学报, 2013, 41(10): 2041-2050.

    Wang Q, Li J, Shen Y. A survey on deterministic measurement matrix construction algorithms in compressive sensing[J]. Acta Electronica Sinica, 2013, 41(10): 2041-2050.

[26] Obermeier R. Martinez-Lorenzo J A. Sensing matrix design via capacity maximization for block compressive sensing applications[J]. IEEE Transactions on Computational Imaging, 2019, 5(1): 27-36.

[27] Zelnik-Manor L, Rosenblum K, Eldar Y C. Sensing matrix optimization for block-sparse decoding[J]. IEEE Transactions on Signal Processing, 2011, 59(9): 4300-4312.

[28] Obermeier R. Martinez-Lorenzo J A. Sensing matrix design via mutual coherence minimization for electromagnetic compressive imaging applications[J]. IEEE Transactions on Computational Imaging, 2017, 3(2): 217-229.

[29] Adcock B, Hansen A C, Poon C, et al. Breaking the coherence barrier: a new theory for compressed sensing[J]. Forum of Mathematics, Sigma, 2017, 5: e4.

[30] JolliffeI. Principal component analysis[M] //Lovric M. International encyclopedia of statistical science. Berlin, Heidelberg: Springer, 2011.

[31] Ke J, Lam E Y. Fast compressive measurements acquisition using optimized binary sensing matrices for low-light-level imaging[J]. Optics Express, 2016, 24(9): 9869-9887.

[32] Ke J, Lam E Y. Object reconstruction in block-based compressive imaging[J]. Optics Express, 2012, 20(20): 22102-22117.

[33] ChenH, Asif MS, Sankaranarayanan AC, et al. FPA-CS: focal plane array-based compressive imaging in short-wave infrared[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 2015: 2358- 2366.

[34] MunS, Fowler JE. Block compressed sensing of images using directional transforms[C]//2009 16th IEEE international conference on image processing (ICIP), November 7-10, 2009, Cairo, Egypt. New York: IEEE, 2009: 3021- 3024.

[35] GanL. Block compressed sensing of natural images[C]//2007 15th International Conference on Digital Signal Processing, July 1-4, 2007, Cardiff, UK. New York: IEEE, 2007: 403- 406.

[36] Neifeld M A, Ke J. Optical architectures for compressive imaging[J]. Applied Optics, 2007, 46(22): 5293-5303.

[37] RothS, Black MJ. Fields of experts: a framework for learning image priors[C]//2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), June 20-25, 2005, San Diego, CA, USA. New York: IEEE, 2005: 8624117.

[38] Roth S, Black M J. Fields of experts[J]. International Journal of Computer Vision, 2009, 82(2): 205-229.

[39] DabovK, FoiA, KatkovnikV, et al. BM3D image denoising with shape-adaptive principal component analysis[C]//SPARS'09-Signal Processing with Adaptive Sparse Structured Representations, Inria Rennes-Bretagne Atlantique, April 2009, Saint Malo, France. [S.l.: s.n.], 2009.

[40] Burger HC, Schuler CJ, HarmelingS. Image denoising: can plain neural networks compete with BM3D?[C]//2012 IEEE Conference on Computer Vision and Pattern Recognition, June 16-21, 2012, Providence, RI, USA. New York: IEEE, 2012: 2392- 2399.

[41] Dumas J P, Lodhi M A, Bajwa W U, et al. Computational imaging with a highly parallel image-plane-coded architecture: challenges and solutions[J]. Optics Express, 2016, 24(6): 6145-6155.

[42] Kaylor BM, AshokA, Seger EM, et al. Dynamically programmable, dual-band computational imaging system[C]//Imaging and Applied Optics Technical Papers, June 24-28, 2012, Monterey, California, United States. Washington, D.C.: OSA, 2012: CM4B. 3.

[43] Mahalanobis A, Shilling R, Murphy R, et al. Recent results of medium wave infrared compressive sensing[J]. Applied Optics, 2014, 53(34): 8060-8070.

[44] Todd D, Sanjeev A, Judith D, et al. An overview of joint activities on computational imaging and compressive sensing systems by NATO SET-232[J]. Proceedings of SPIE, 2018, 10669: 106690H.

[45] Kang LW, Lu CS. Distributed compressive video sensing[C]//2009 IEEE International Conference on Acoustics, Speech and Signal Processing, April 19-24, 2009, Taipei, Taiwan, China. New York: IEEE, 2009: 1169- 1172.

[46] WakinM, Laska JN, Duarte MF, et al. and coding[C/OL]. [S.l.:s.n.], 2006[ 2019-09-08]. https://www.researchgate.net/publication/220043723_Compressive_Imaging_for_Video_Representation_and_Coding.

[47] Sankaranarayanan A C, Xu L N, Studer C, et al. Video compressive sensing for spatial multiplexing cameras using motion-flow models[J]. SIAM Journal on Imaging Sciences, 2015, 8(3): 1489-1518.

[48] Fowler J E, Mun S, Tramel E W. Block-based compressed sensing of images and video[J]. Foundations and Trends © in Signal Processing, 2010, 4(4): 297-416.

[49] Raginsky M, Willett R M, Harmany Z T, et al. Compressed sensing performance bounds under Poisson noise[J]. IEEE Transactions on Signal Processing, 2010, 58(8): 3990-4002.

[50] Amann M C, Bosch T M, Lescure M, et al. Laser ranging: a critical review of usual techniques for distance measurement[J]. Optical Engineering, 2001, 40(1): 10-19.

[51] Lucas BD, KanadeT. An iterative image registration technique with an application to stereo vision[C]//Proc 17th Intl Joint Conf on Artificial Intelligence(IJCAI) 1981, August 24-28, 1981, Vancouver, British Columbia. [S.l.: s.n.], 1981: 674- 679.

[52] Geng J. Structured-light 3D surface imaging: a tutorial[J]. Advances in Optics and Photonics, 2011, 3(2): 128-160.

[53] Han J, Shao L, Xu D, et al. Enhanced computer vision with microsoft kinect sensor: a review[J]. IEEE Transactions on Cybernetics, 2013, 43(5): 1318-1334.

[54] Kirmani A, Colaço A. Wong F N C, et al. Exploiting sparsity in time-of-flight range acquisition using a single time-resolved sensor[J]. Optics Express, 2011, 19(22): 21485-21507.

[55] Howland G A, Dixon P B, Howell J C. Photon-counting compressive sensing laser radar for 3D imaging[J]. Applied Optics, 2011, 50(31): 5917-5920.

[56] Li L, Wu L, Wang X B, et al. Gated viewing laser imaging with compressive sensing[J]. Applied Optics, 2012, 51(14): 2706-2712.

[57] Howland G A, Lum D J, Ware M R, et al. Photon counting compressive depth mapping[J]. Optics Express, 2013, 21(20): 23822-23837.

[58] Ren X M, Li L, Dang E S. Compressive sampling and gated viewing three-dimensional laser radar[J]. Journal of Physics: Conference Series, 2011, 276(1): 012142.

[59] Sun M J, Edgar M P, Gibson G M, et al. Single-pixel three-dimensional imaging with time-based depth resolution[J]. Nature Communications, 2016, 7: 12010.

[60] Li F Q, Chen H J, Pediredla A, et al. CS-ToF: high-resolution compressive time-of-flight imaging[J]. Optics Express, 2017, 25(25): 31096-31110.

[61] Babbitt W R, Barber Z W, Renner C. Compressive laser ranging[J]. Optics Letters, 2011, 36(24): 4794-4796.

[62] KeJ, Lam EY. Temporal super-resolution full waveform LiDAR[C]//Imaging and Applied Optics 2018 (3D, AO, AIO, COSI, DH, IS, LACSEA, LS&C, MATH, pcAOP), June 25-28, 2018, Orlando, Florida, United States. Washington, D.C.: OSA, 2018: CTh3C. 1.

[63] Llull P, Liao X J, Yuan X, et al. Coded aperture compressive temporal imaging[J]. Optics Express, 2013, 21(9): 10526-10545.

[64] Koller R, Schmid L, Matsuda N, et al. High spatio-temporal resolution video with compressed sensing[J]. Optics Express, 2015, 23(12): 15992-16007.

[65] Yuan X, Sun Y Y, Pang S. Compressive video sensing with side information[J]. Applied Optics, 2017, 56(10): 2697-2704.

[66] Chen Y T, Tang C Y, Xu Z H, et al. Adaptive reconstruction for coded aperture temporal compressive imaging[J]. Applied Optics, 2017, 56(17): 4940-4947.

[67] Zhou Q, Ke J, Lam E Y. Near-infrared temporal compressive imaging for video[J]. Optics Letters, 2019, 44(7): 1702-1705.

[68] ZhouQ, KeJ, Lam EY. Dual-waveband temporal compressive imaging[C]//Imaging and Applied Optics 2019 (COSI, IS, MATH, pcAOP), June 24-27, 2019, Munich, Germany. Washington, D.C.: OSA, 2019: CTu2A. 8.

[69] Yang J B, Yuan X, Liao X J, et al. Video compressive sensing using Gaussian mixture models[J]. IEEE Transactions on Image Processing, 2014, 23(11): 4863-4878.

[70] Liu Y, Yuan X, Suo J L, et al. Rank minimization for snapshot compressive imaging[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41(12): 2990-3006.

[71] Zhang J, Xiong T, Tran T, et al. Compact all-CMOS spatiotemporal compressive sensing video camera with pixel-wise coded exposure[J]. Optics Express, 2016, 24(8): 9013-9024.

[72] Courtney-Pratt J S. A review of the methods of high-speed photography[J]. Reports on Progress in Physics, 1957, 20(1): 379-432.

[73] Field J E. High-speed photography[J]. Contemporary Physics, 1983, 24(5): 439-459.

[74] Liang J Y, Wang L V. Single-shot ultrafast optical imaging[J]. Optica, 2018, 5(9): 1113-1127.

[75] Gao L, Liang J Y, Li C Y, et al. Single-shot compressed ultrafast photography at one hundred billion frames per second[J]. Nature, 2014, 516(7529): 74-77.

[76] Mikami H, Gao L, Goda K. Ultrafast optical imaging technology: principles and applications of emerging methods[J]. Nanophotonics, 2016, 5(4): 497-509.

[77] Arce G R, Brady D J, Carin L, et al. Compressive coded aperture spectral imaging: an introduction[J]. IEEE Signal Processing Magazine, 2014, 31(1): 105-115.

[78] Wagadarikar A, John R, Willett R, et al. Single disperser design for coded aperture snapshot spectral imaging[J]. Applied Optics, 2008, 47(10): B44-B51.

[79] Correa C V, Arguello H, Arce G R. Snapshot colored compressive spectral imager[J]. Journal of the Optical Society of America A, 2015, 32(10): 1754-1763.

[80] Fu C, Don M L, Arce G R. Compressive spectral imaging via polar coded aperture[J]. IEEE Transactions on Computational Imaging, 2017, 3(3): 408-420.

[81] Galvis L, Lau D, Ma X, et al. Coded aperture design in compressive spectral imaging based on side information[J]. Applied Optics, 2017, 56(22): 6332-6340.

[82] Mao T Y, Cuadros A, Ma X, et al. Coded aperture optimization in X-ray tomography via sparse principal component analysis[J]. IEEE Transactions on Computational Imaging, 2019, 1.

[83] Parada-Mayorga A, Arce G R. Colored coded aperture design in compressive spectral imaging via minimum coherence[J]. IEEE Transactions on Computational Imaging, 2017, 3(2): 202-216.

[84] Arguello H, Arce G R. Rank minimization code aperture design for spectrally selective compressive imaging[J]. IEEE Transactions on Image Processing, 2012, 22(3): 941-954.

[85] Correa C V, Arguello H, Arce G R. Spatiotemporal blue noise coded aperture design for multi-shot compressive spectral imaging[J]. Journal of the Optical Society of America A, 2016, 33(12): 2312-2322.

[86] Bian L, Suo J, Situ G, et al. Multispectral imaging using a single bucket detector[J]. Scientific Reports, 2016, 6: 24752.

[87] Arnob M M P, Nguyen H, Han Z, et al. Compressed sensing hyperspectral imaging in the 0.9-2.5 μm shortwave infrared wavelength range using a digital micromirror device and InGaAs linear array detector[J]. Applied Optics, 2018, 57(18): 5019-5024.

[88] Padgett M J, Boyd R W. An introduction to ghost imaging: quantum and classical[J]. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2017, 375(2099): 20160233.

[89] Shapiro J H. Computational ghost imaging[J]. Physical Review A, 2008, 78(6): 061802.

[90] Katz O, Bromberg Y, Silberberg Y. Compressive ghost imaging[J]. Applied Physics Letters, 2009, 95(13): 131110.

[91] Bromberg Y, Katz O, Silberberg Y. Ghost imaging with a single detector[J]. Physical Review A, 2009, 79(5): 053840.

[92] Erkmen B I, Shapiro J H. Ghost imaging: from quantum to classical to computational[J]. Advances in Optics and Photonics, 2010, 2(4): 405-450.

[93] Katkovnik V, Astola J. Compressive sensing computational ghost imaging[J]. Journal of the Optical Society of America A, 2012, 29(8): 1556-1567.

[94] Erkmen B I. Computational ghost imaging for remote sensing[J]. Journal of the Optical Society of America A, 2012, 29(5): 782-789.

[95] Durán V, Soldevila F, Irles E, et al. Compressive imaging in scattering media[J]. Optics Express, 2015, 23(11): 14424-14433.

[96] Gong W, Zhao C, Yu H, et al. Three-dimensional ghost imaging lidar via sparsity constraint[J]. Scientific Reports, 2016, 6: 26133.

[97] Zhao C, Gong W, Chen M, et al. Ghost imaging lidar via sparsity constraints[J]. Applied Physics Letters, 2012, 101(14): 141123.

[98] Gong W L, Han S S. High-resolution far-field ghost imaging via sparsity constraint[J]. Scientific Reports, 2015, 5: 9280.

[99] Yu W K, Li M F, Yao X R, et al. Adaptive compressive ghost imaging based on wavelet trees and sparse representation[J]. Optics Express, 2014, 22(6): 7133-7144.

[100] Shi D F, Hu S X, Wang Y J. Polarimetric ghost imaging[J]. Optics Letters, 2014, 39(5): 1231-1234.

[101] Zhao S, Wang L, Liang W, et al. High performance optical encryption based on computational ghost imaging with QR code and compressive sensing technique[J]. Optics Communications, 2015, 353: 90-95.

[102] Lyu M, Wang W, Wang H, et al. Deep-learning-based ghost imaging[J]. Scientific Reports, 2017, 7: 17865.

[103] Wang F, Wang H, Wang H C, et al. Learning from simulation: an end-to-end deep-learning approach for computational ghost imaging[J]. Optics Express, 2019, 27(18): 25560-25572.

[104] Brady D J, Choi K, Marks D L, et al. Compressive holography[J]. Optics Express, 2009, 17(15): 13040-13049.

[105] Cull C F, Wikner D A, Mait J N, et al. Millimeter-wave compressive holography[J]. Applied Optics, 2010, 49(19): E67-E82.

[106] Qiao L B, Wang Y X, Shen Z J, et al. Compressive sensing for direct millimeter-wave holographic imaging[J]. Applied Optics, 2015, 54(11): 3280-3289.

[107] Rivenson Y, Stern A, Javidi B. Overview of compressive sensing techniques applied in holography [Invited][J]. Applied Optics, 2013, 52(1): A423-A432.

[108] Chen W S, Tian L, Rehman S, et al. Empirical concentration bounds for compressive holographic bubble imaging based on a Mie scattering model[J]. Optics Express, 2015, 23(4): 4715-4725.

[109] Wang Z H, Spinoulas L, He K, et al. Compressive holographic video[J]. Optics Express, 2017, 25(1): 250-262.

[110] Endo Y, Shimobaba T, Kakue T, et al. GPU-accelerated compressive holography[J]. Optics Express, 2016, 24(8): 8437-8445.

[111] Rivenson Y, Wu Y C, Wang H D, et al. Sparsity-based multi-height phase recovery in holographic microscopy[J]. Scientific Reports, 2016, 6: 37862.

[112] Zhang W H, Cao L C, Brady D J, et al. Twin-image-free holography: a compressive sensing approach[J]. Physical Review Letters, 2018, 121(9): 093902.

[113] Lohit S, Kulkarni K, Kerviche R, et al. Convolutional neural networks for noniterative reconstruction of compressively sensed images[J]. IEEE Transactions on Computational Imaging, 2018, 4(3): 326-340.

[114] IliadisM, SpinoulasL, Katsaggelos A K. Deep Binary Mask: learning a binary mask for video compressive sensing[J/OL]. ( 2016-07-18)[2019-09-08]. top/abs/1607. 03343. https://arxiv.xilesou.

[115] Iliadis M, Spinoulas L, Katsaggelos A K. Deep fully-connected networks for video compressive sensing[J]. Digital Signal Processing, 2018, 72: 9-18.

[116] Mahalanobis A, Shilling R, Muise R, et al. High-resolution imaging using a translating coded aperture[J]. Optical Engineering, 2017, 56(8): 084106.

[117] Tsai T H, Llull P, Yuan X, et al. Spectral-temporal compressive imaging[J]. Optics Letters, 2015, 40(17): 4054-4057.

[118] Sun Y Y, Yuan X, Pang S. Compressive high-speed stereo imaging[J]. Optics Express, 2017, 25(15): 18182-18190.

[119] Zhang Z B, Liu S J, Peng J Z, et al. Simultaneous spatial, spectral, and 3D compressive imaging via efficient Fourier single-pixel measurements[J]. Optica, 2018, 5(3): 315-319.

柯钧, 张临夏, 周群. 压缩感知在光学成像领域的应用[J]. 光学学报, 2020, 40(1): 0111006. Jun Ke, Linxia Zhang, Qun Zhou. Applications of Compressive Sensing in Optical Imaging[J]. Acta Optica Sinica, 2020, 40(1): 0111006.

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