[1] [1] Boyle WS, Smith GE. Charge coupled semiconductor devices. Bell Syst Tech J. 1970;49(4):587–93.10.1002/j.1538-7305.1970.tb01790.x

[2] [2] Altmann Y, McLaughlin S, Padgett MJ, Goyal VK, Hero AO, Faccio D. Quantum-inspired computational imaging. Science. 2018;361(6403):eaat2298. .

[3] [3] Mait JN, Euliss GW, Athale RA. Computational imaging. Adv Opt Photonics. 2018;10(2):409–83.10.1364/AOP.10.000409

[4] [4] Yuan X, Brady DJ, Katsaggelos AK. Snapshot compressive imaging: theory, algorithms, and applications. IEEE Signal Process Mag. 2021;38(2):65–88.10.1109/MSP.2020.3023869

[5] [5] Gao L, Liang J, Li C, Wang LV. Single-shot compressed ultrafast photography at one hundred billion frames per second. Nature. 2014;516(7529):74–7.10.1038/nature14005

[6] [6] Raskar R, Agrawal A, Tumblin J. Coded exposure photography: motion deblurring using fluttered shutter. ACM Trans Graphics. 2006;25(3):795–804.10.1145/1141911.1141957

[7] [7] Sitzmann V, Diamond S, Peng Y, Dun X, Boyd S, Heidrich W, et al. End-to-end optimization of optics and image processing for achromatic extended depth of field and super-resolution imaging. ACM Trans Graphics. 2018;37(4):1–13.10.1145/3197517.3201333

[8] [8] Sun Q, Zhang J, Dun X, Ghanem B, Peng Y, Heidrich W. End-to-end learned, optically coded super-resolution SPAD camera. ACM Trans Graph. 2020;39(2):1–14.10.1145/3372261

[9] [9] Antipa N, Oare P, Bostan E, Ng R, Waller L. Video from stills: lensless imaging with rolling shutter. In: 2019 IEEE International Conference on Computational Photography (ICCP). IEEE; 2019. p. 1-8.

[10] [10] Asif MS, Ayremlou A, Sankaranarayanan A, Veeraraghavan A, Baraniuk RG. FlatCam: thin, lensless cameras using coded aperture and computation. IEEE Trans Comput Imaging. 2017;3(3):384–97.10.1109/TCI.2016.2593662

[11] [11] Cai Z, Chen J, Pedrini G, Osten W, Liu X, Peng X. Lensless light-field imaging through diffuser encoding. Light Sci Appl. 2020;9(1):143.10.1038/s41377-020-00380-x

[12] [12] Hu C, Huang H, Chen M, Yang S, Chen H. FourierCam: a camera for video spectrum acquisition in a single shot. Photon Res. 2021;9(5):701.10.1364/PRJ.412491

[13] [13] Liang CK, Lin TH, Wong BY, Liu C, Chen HH. Programmable aperture photography: multiplexed light field acquisition. ACM Trans Graph. 2008;27(3):391–400.10.1145/1360612.1360654

[14] [14] Lv X, Li Y, Zhu S, Guo X, Zhang J, Lin J, et al. Snapshot spectral polarimetric light field imaging using a single detector. Opt Lett. 2020;45(23):6522.10.1364/OL.409476

[15] [15] Hu C, Huang H, Chen M, Yang S, Chen H. Video object detection from one single image through opto-electronic neural network. APL Photon. 2021;6(4):046104.10.1063/5.0040424

[16] [16] Okawara T, Yoshida M, Nagahara H, Yagi Y. Action recognition from a single coded image. In: 2020 IEEE International Conference on Computational Photography (ICCP). IEEE; 2020. p. 1-11.

[17] [17] Wu Y, Boominathan V, Chen H, Sankaranarayanan A, Veeraraghavan A. PhaseCam3D — learning phase masks for passive single view depth estimation. In: 2019 IEEE International Conference on Computational Photography (ICCP). IEEE; 2019. p. 1-12.

[18] [18] Audebert N, Le Saux B, Lefevre S. Deep learning for classification of hyperspectral data: a comparative review. IEEE Geosci Remote Sens Mag. 2019;7(2):159–73.10.1109/MGRS.2019.2912563

[19] [19] Rawat W, Wang Z. Deep convolutional neural networks for image classification: a comprehensive review. Neural Comput. 2017;29(9):2352–449.10.1162/neco_a_00990

[20] [20] Asgari Taghanaki S, Abhishek K, Cohen JP, Cohen-Adad J, Hamarneh G. Deep semantic segmentation of natural and medical images: a review. Artif Intell Rev. 2021;54(1):137–78.10.1007/s10462-020-09854-1

[21] [21] Yu H, Yang Z, Tan L, Wang Y, Sun W, Sun M, et al. Methods and datasets on semantic segmentation: a review. Neurocomputing. 2018;304:82–103.10.1016/j.neucom.2018.03.037

[22] [22] Jiao L, Wang D, Bai Y, Chen P, Liu F. Deep learning in visual tracking: a review. IEEE Trans Neural Netw Learn Syst. 2021;1(1):1–20.

[23] [23] Pal SK, Pramanik A, Maiti J, Mitra P. Deep learning in multi-object detection and tracking: state of the art. Appl Intell. 2021;51(9):6400–29.10.1007/s10489-021-02293-7

[24] [24] Zhu H, Wei H, Li B, Yuan X, Kehtarnavaz N. A review of video object detection: datasets, metrics and methods. Appl Sci. 2020;10(21):7834.10.3390/app10217834

[25] [25] Aafaq N, Mian A, Liu W, Gilani SZ, Shah M. Video description: a survey of methods, datasets, and evaluation metrics. ACM Comput Surv. 2020;52(6):1–37.10.1145/3355390

[26] [26] Hossain MZ, Sohel F, Shiratuddin MF, Laga H. A comprehensive survey of deep learning for image captioning. ACM Comput Surv. 2019;51(6):1–36.10.1145/3295748

[27] [27] Guo Y, Liu Y, Georgiou T, Lew MS. A review of semantic segmentation using deep neural networks. Int J Multimed Info Retr. 2018;7(2):87–93.10.1007/s13735-017-0141-z

[28] [28] Herath S, Harandi M, Porikli F. Going deeper into action recognition: a survey. Image Vision Comput. 2017;60:4–21.10.1016/j.imavis.2017.01.010

[29] [29] Li S, Deng W. Deep facial expression recognition: a survey. IEEE Trans Affect Comput. 2020;1(1):1–10.

[30] [30] Pawar PG, Devendran V. Scene understanding: a survey to see the world at a single glance. In: 2019 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT). IEEE; 2019. p. 182-6.

[31] [31] Chen S, Yao T, Jiang YG. Deep learning for video captioning: a review. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI). International Joint Conferences on Artificial Intelligence Organization; 2019. p. 6283-90.

[32] [32] Deng C, Zhang Y, Mao Y, Fan J, Suo J, Zhang Z, et al. Sinusoidal sampling enhanced compressive camera for high speed imaging. IEEE Trans Pattern Anal Mach Intell. 2021;43(4):1380–93.10.1109/TPAMI.2019.2946567

[33] [33] Hitomi Y, Gu J, Gupta M, Mitsunaga T, Nayar SK. Video from a single coded exposure photograph using a learned over-complete dictionary. In: 2011 International Conference on Computer Vision (ICCV). IEEE; 2011. p. 287-94.

[34] [34] Llull P, Liao X, Yuan X, Yang J, Kittle D, Carin L, et al. Coded aperture compressive temporal imaging. Opt Express. 2013;21(9):10526.10.1364/OE.21.010526

[35] [35] Lu R, Chen B, Liu G, Cheng Z, Qiao M, Yuan X. Dual-view snapshot compressive imaging via optical flow aided recurrent neural network. Int J Comput Vision. 2021;129(12):3279–98.10.1007/s11263-021-01532-1

[36] [36] Qiao M, Liu X, Yuan X. Snapshot spatial-temporal compressive imaging. Opt Lett. 2020;45(7):1659–62.10.1364/OL.386238

[37] [37] Qiao M, Meng Z, Ma J, Yuan X. Deep learning for video compressive sensing. APL Photonics. 2020;5(3):030801.10.1063/1.5140721

[38] [38] Reddy D, Veeraraghavan A, Chellappa R. P2C2: programmable pixel compressive camera for high speed imaging. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE; 2011. p. 329-36.

[39] [39] Shedligeri P, S A, Mitra K. A unified framework for compressive video recovery from coded exposure techniques. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE; 2021. p. 1600-9.

[40] [40] Yoshida M, Sonoda T, Nagahara H, Endo K, Sugiyama Y, Taniguchi RI. High-speed imaging using CMOS image sensor with quasi pixel-wise exposure. IEEE Trans Comput Imaging. 2020;6:463–76.10.1109/TCI.2019.2956885

[41] [41] Yuan X, Llull P, Liao X, Yang J, Brady DJ, Sapiro G, et al. Low-cost compressive sensing for color video and depth. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE; 2014. p. 3318-25.

[42] [42] Zhang Z, Deng C, Liu Y, Yuan X, Suo J, Dai Q. Ten-mega-pixel snapshot compressive imaging with a hybrid coded aperture. Photonics Res. 2021;9(11):2277.10.1364/PRJ.435256

[43] [43] Wei M, Sarhangnejad N, Xia Z, Gusev N, Katic N, Genov R, et al. Coded two-bucket cameras for computer vision. In: European Conference on Computer Vision (ECCV). Springer; 2018. p. 54-71.

[44] [44] Wang P, Liang J, Wang LV. Single-shot ultrafast imaging attaining 70 trillion frames per second. Nat Commun. 2020;11(1):2091.10.1038/s41467-020-15745-4

[45] [45] Liu Y, Yuan X, Suo J, Brady DJ, Dai Q. Rank minimization for snapshot compressive imaging. IEEE Trans Pattern Anal Mach Intell. 2019;41(12):2990–3006.10.1109/TPAMI.2018.2873587

[46] [46] Yuan X. Generalized alternating projection based total variation minimization for compressive sensing. In: 2016 IEEE International Conference on Image Processing (ICIP). IEEE; 2016. p. 2539-43.

[47] [47] Yuan X, Liu Y, Suo J, Dai Q. Plug-and-play algorithms for large-scale snapshot compressive imaging. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE; 2020. p. 1444-54.

[48] [48] Jalali S, Yuan X. Snapshot compressed sensing: performance bounds and algorithms. IEEE Trans Inf Theory. 2019;65(12):8005–24.10.1109/TIT.2019.2940666

[49] [49] Jalali S, Yuan X, Compressive imaging via one-shot measurements. In: 2018 IEEE International Symposium on Information Theory (ISIT). IEEE; 2018. p. 416–20.

[50] [50] Bioucas-Dias JM, Figueiredo MAT. A new TwIST: two-step iterative shrinkage/thresholding algorithms for image restoration. IEEE Trans Image Process. 2007;16(12):2992–3004.10.1109/TIP.2007.909319

[51] [51] Cheng Z, Lu R, Wang Z, Zhang H, Chen B, Meng Z, et al. BIRNAT: bidirectional recurrent neural networks with adversarial training for video snapshot compressive imaging. In: European Conference on Computer Vision (ECCV). Springer; 2020. p. 258-75.

[52] [52] Iliadis M, Spinoulas L, Katsaggelos AK. Deep fully-connected networks for video compressive sensing. Digit Signal Process. 2018;72:9–18.10.1016/j.dsp.2017.09.010

[53] [53] Ma J, Liu XY, Shou Z, Yuan X. Deep tensor ADMM-Net for snapshot compressive imaging. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). IEEE; 2019. p. 10222-31.

[54] [54] Wang Z, Zhang H, Cheng Z, Chen B, Yuan X. MetaSCI: scalable and adaptive reconstruction for video compressive sensing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE; 2021. p. 2083-92.

[55] [55] Wu Z, Zhang J, Mou C. Dense deep unfolding network with 3D-CNN prior for snapshot compressive imaging. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). IEEE; 2021. p. 4892-901.

[56] [56] Yang J, Liao X, Yuan X, Llull P, Brady DJ, Sapiro G, et al. Compressive sensing by learning a Gaussian mixture model from measurements. IEEE Trans Image Process. 2015;24(1):106–19.10.1109/TIP.2014.2365720

[57] [57] Yang J, Yuan X, Liao X, Llull P, Brady DJ, Sapiro G, et al. Video compressive sensing using Gaussian mixture models. IEEE Trans Image Process. 2014;23(11):4863–78.10.1109/TIP.2014.2344294

[58] [58] Cheng Z, Chen B, Liu G, Zhang H, Lu R, Wang Z, et al. Memory-efficient network for large-scale video compressive sensing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE; 2021. p. 16246-55.

[59] [59] Yuan X, Liu Y, Suo J, Durand F, Dai Q. Plug-and-play algorithms for video snapshot compressive imaging. IEEE Trans Pattern Anal Mach Intell. 2021;1(1):1–18.10.1109/TPAMI.2021.3099035

[60] [60] Liao X, Li H, Carin L. Generalized alternating projection for weighted-\(\ell _{2,1}\) minimization with applications to model-based compressive sensing. SIAM J Imaging Sci. 2014;7(2):797–823.

[61] [61] Boyd S. Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trends® Mach Learn. 2010;3(1):1–122.

[62] [62] Bethi YRT, Narayanan S, Rangan V, Chakraborty A, Thakur CS. Real-time object detection and localization in compressive sensed video. In: 2021 IEEE International Conference on Image Processing (ICIP). IEEE; 2021. p. 1489-93.

[63] [63] Kwan C, Chou B, Yang J, Rangamani A, Tran T, Zhang J, et al. Target tracking and classification using compressive measurements of MWIR and LWIR coded aperture cameras. J Signal Inf Process. 2019;10(03):73–95.

[64] [64] Lu S, Yuan X, Shi W, Edge compression: an integrated framework for compressive imaging processing on CAVs. In: 2020 IEEE/ACM Symposium on Edge Computing (SEC). IEEE; 2020. p. 125–38.

[65] [65] Kwan C, Chou B, Yang J, Rangamani A, Tran T, Zhang J, et al. Deep learning-based target tracking and classification for low quality videos using coded aperture cameras. Ah S Sens. 2019;19(17):3702.10.3390/s19173702

[66] [66] Kwan C, Chou B, Yang J, Rangamani A, Tran T, Zhang J, et al. Target tracking and classification using compressive sensing camera for SWIR videos. Signal Image Video Process. 2019;13(8):1629–37.10.1007/s11760-019-01506-4

[67] [67] Rezaei M, Terauchi M, Klette R. Robust vehicle detection and distance estimation under challenging lighting conditions. IEEE Trans Intell Transp Syst. 2015;16(5):2723–43.10.1109/TITS.2015.2421482

[68] [68] Zhe T, Huang L, Wu Q, Zhang J, Pei C, Li L. Inter-vehicle distance estimation method based on monocular vision using 3D detection. IEEE Trans Veh Technol. 2020;69(5):4907–19.10.1109/TVT.2020.2977623

[69] [69] Yuan X, Yang J, Llull P, Liao X, Sapiro G, Brady DJ, et al. Adaptive temporal compressive sensing for video. In: 2013 IEEE International Conference on Image Processing (ICIP). IEEE; 2013. p. 14-8.

[70] [70] Zheng S, Wang C, Yuan X, Xin HL. Super-compression of large electron microscopy time series by deep compressive sensing learning. Patterns. 2021;2(7):100292.10.1016/j.patter.2021.100292

[71] [71] Zheng S, Yang X, Yuan X. Two-stage is enough: a concise deep unfolding reconstruction network for flexible video compressive sensing. arXiv preprint arXiv:2201.05810. 2022;1(1):1-10.

[72] [72] Gomez AN, Ren M, Urtasun R, Grosse RB. The reversible residual network: backpropagation without storing activations. In: Proceedings of the International Conference on Neural Information Processing Systems (NeurIPS). vol. 30. Curran Associates, Inc.; 2017. p. 1-10.

[73] [73] Zhou B, Lapedriza A, Khosla A, Oliva A, Torralba A. Places: a 10 million image database for scene recognition. IEEE Trans Pattern Anal Mach Intell. 2018;40(6):1452–64.10.1109/TPAMI.2017.2723009

[74] [74] Zhou X, Koltun V, Krähenbühl P. Tracking objects as points. In: European Conference on Computer Vision (ECCV). Springer; 2020. p. 474-90.

[75] [75] Caesar H, Bankiti V, Lang AH, Vora S, Liong VE, Xu Q, et al. nuScenes: a multimodal dataset for autonomous driving. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE; 2020. p. 11618-28.

[76] [76] Hu W, Tan T, Wang L, Maybank S. A survey on visual surveillance of object motion and behaviors. IEEE Syst Man Cybern Mag. 2004;34(3):334–52.10.1109/TSMCC.2004.829274

[77] [77] Zhao ZQ, Zheng P, Xu St, Wu X. Object detection with deep learning: a review. IEEE Trans Neural Netw Learn Syst. 2019;30(11):3212-32.

[78] [78] Feng D, Haase-Schütz C, Rosenbaum L, Hertlein H, Glaeser C, Timm F, et al. Deep multi-modal object detection and semantic segmentation for autonomous driving: datasets, methods, and challenges. IEEE Trans Intell Transp Syst. 2020;22(3):1341–60.10.1109/TITS.2020.2972974