[1] P. R. Poulin, K. A. Nelson. Irreversible organic crystalline chemistry monitored in real time. Science, 313, 1756-1760(2006).

[2] P. Hockett, C. Z. Bisgaard, O. J. Clarkin, A. Stolow. Time-resolved imaging of purely valence-electron dynamics during a chemical reaction. Nat. Phys., 7, 612-615(2011).

[3] R. Horstmeyer, H. Ruan, C. Yang. Guidestar-assisted wavefront-shaping methods for focusing light into biological tissue. Nat. Photonics, 9, 563-571(2015).

[4] J. W. Borst, A. J. Visser. Fluorescence lifetime imaging microscopy in life sciences. Meas. Sci. Technol., 21, 102002(2010).

[5] H. R. Petty. Spatiotemporal chemical dynamics in living cells: from information trafficking to cell physiology. Biosystems, 83, 217-224(2006).

[6] T. M. Squires, S. R. Quake. Microfluidics: fluid physics at the nanoliter scale. Rev. Mod. Phys., 77, 977-1026(2005).

[7] N. Šiaulys, L. Gallais, A. Melninkaitis. Direct holographic imaging of ultrafast laser damage process in thin films. Opt. Lett., 39, 2164-2167(2014).

[8] R. L. Kodama, P. A. Norreys, K. Mima, A. E. Dangor, R. G. Evans, H. Fujita, Y. Kitagawa, K. Krushelnick, T. Miyakoshi, N. Miyanaga. Fast heating of ultrahigh-density plasma as a step towards laser fusion ignition. Nature, 412, 798-802(2001).

[9] L. Gao, J. Liang, C. Li, L. V. Wang. Single-shot compressed ultrafast photography at one hundred billion frames per second. Nature, 516, 74-77(2014).

[10] J. Liang, L. Zhu, L. V. Wang. Single-shot real-time femtosecond imaging of temporal focusing. Light Sci. Appl., 7, 42(2018).

[11] D. Qi, S. Zhang, C. Yang, Y. He, F. Cao, J. Yao, P. Ding, L. Gao, T. Jia, J. Liang. Single-shot compressed ultrafast photography: a review. Adv. Photon., 2, 014003(2020).

[12] K. Nakagawa, A. Iwasaki, Y. Oishi, R. Horisaki, A. Tsukamoto, A. Nakamura, K. Hirosawa, H. Liao, T. Ushida, K. Goda. Sequentially timed all-optical mapping photography (STAMP). Nat. Photonics, 8, 695-700(2014).

[13] T. Suzuki, R. Hida, Y. Yamaguchi, K. Nakagawa, T. Saiki, F. Kannari. Single-shot 25-frame burst imaging of ultrafast phase transition of Ge₂Sb₂Te₅ with a sub-picosecond resolution. Appl. Phys. Express, 10, 092502(2017).

[14] Y. Lu, T. T. Wong, F. Chen, L. Wang. Compressed ultrafast spectral-temporal photography. Phys. Rev. Lett., 122, 193904(2019).

[15] A. Velten, T. Willwacher, O. Gupta, A. Veeraraghavan, M. G. Bawendi, R. Raskar. Recovering three-dimensional shape around a corner using ultrafast time-of-flight imaging. Nat. Commun., 3, 745(2012).

[16] A. H. Zewail. Four-dimensional electron microscopy. Science, 328, 187-193(2010).

[17] A. Barty, S. Boutet, M. J. Bogan, S. Hau-Riege, S. Marchesini, K. Sokolowski-Tinten, N. Stojanovic, H. Ehrke, A. Cavalleri, S. Düsterer. Ultrafast single-shot diffraction imaging of nanoscale dynamics. Nat. Photonics, 2, 415-419(2008).

[18] J. Liang, C. Ma, L. Zhu, Y. Chen, L. Gao, L. V. Wang. Single-shot real-time video recording of a photonic Mach cone induced by a scattered light pulse. Sci. Adv., 3, e1601814(2017).

[19] J. C. Jing, X. Wei, L. V. Wang. Spatio-temporal-spectral imaging of non-repeatable dissipative soliton dynamics. Nat. Commun., 11, 2059(2020).

[20] T. Kim, J. Liang, L. Zhu, L. V. Wang. Picosecond-resolution phase-sensitive imaging of transparent objects in a single shot. Sci. Adv., 6, e6200(2020).

[21] J. Liang, L. Gao, P. Hai, C. Li, L. V. Wang. Encrypted three-dimensional dynamic imaging using snapshot time-of-flight compressed ultrafast photography. Sci. Rep., 5, 15504(2015).

[22] F. Cao, C. Yang, D. Qi, J. Yao, Y. He, X. Wang, W. Wen, J. Tian, T. Jia, Z. Sun. Single-shot spatiotemporal intensity measurement of picosecond laser pulses with compressed ultrafast photography. Opt. Laser Eng., 116, 89-93(2019).

[23] L. Zhu, Y. Chen, J. Liang, Q. Xu, L. Gao, C. Ma, L. V. Wang. Space-and intensity-constrained reconstruction for compressed ultrafast photography. Optica, 3, 694-697(2016).

[24] C. Yang, D. Qi, F. Cao, Y. He, X. Wang, W. Wen, J. Tian, T. Jia, Z. Sun, S. Zhang. Improving the image reconstruction quality of compressed ultrafast photography via an augmented Lagrangian algorithm. J. Opt., 21, 035703(2019).

[25] Y. Lai, Y. Xue, C. Y. Côté, X. Liu, A. Laramée, N. Jaouen, F. Légaré, L. Tian, J. Liang. Single-shot ultraviolet compressed ultrafast photography. Laser Photon. Rev., 14, 2000122(2020).

[26] C. Yang, D. Qi, X. Wang, F. Cao, Y. He, W. Wen, T. Jia, J. Tian, Z. Sun, L. Gao. Optimizing codes for compressed ultrafast photography by the genetic algorithm. Optica, 5, 147-151(2018).

[27] C. Yang, D. Qi, J. Liang, X. Wang, F. Cao, Y. He, X. Ouyang, B. Zhu, W. Wen, T. Jia. Compressed ultrafast photography by multi-encoding imaging. Laser Phys. Lett., 15, 116202(2018).

[28] C. Li. An efficient algorithm for total variation regularization with applications to the single pixel camera and compressive sensing(2010).

[29] M. V. Afonso, J. M. Bioucas-Dias, M. A. Figueiredo. An augmented Lagrangian approach to the constrained optimization formulation of imaging inverse problems. IEEE Trans. Image Process., 20, 681-695(2010).

[30] Y. Yang, J. Sun, H. Li, Z. Xu. ADMM-CSNet: a deep learning approach for image compressive sensing. IEEE Trans. Pattern Anal., 42, 521-538(2018).

[31] J. Zhang, B. Ghanem. ISTA-Net: interpretable optimization-inspired deep network for image compressive sensing. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1828-1837(2018).

[32] J. Ma, X. Liu, Z. Shou, X. Yuan. Deep tensor ADMM-net for snapshot compressive imaging. Proceedings of the IEEE International Conference on Computer Vision, 10223-10232(2019).

[33] K. Monakhova, J. Yurtsever, G. Kuo, N. Antipa, K. Yanny, L. Waller. Learned reconstructions for practical mask-based lensless imaging. Opt. Express, 27, 28075-28090(2019).

[34] Q. Xie, Q. Zhao, D. Meng, Z. Xu. Kronecker-basis-representation based tensor sparsity and its applications to tensor recovery. IEEE Trans. Pattern Anal. Mach. Intell., 40, 1888-1902(2017).

[35] Y. Wang, J. Peng, Q. Zhao, Y. Leung, X. Zhao, D. Meng. Hyperspectral image restoration via total variation regularized low-rank tensor decomposition. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sensing, 11, 1227-1243(2017).

[36] L. Wang, C. Sun, Y. Fu, M. H. Kim, H. Huang. Hyperspectral image reconstruction using a deep spatial-spectral prior. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 8032-8041(2019).

[37] Z. Wu, Y. Sun, A. Matlock, J. Liu, L. Tian, U. S. Kamilov. SIMBA: scalable inversion in optical tomography using deep denoising priors. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sensing, 14, 1163-1175(2020).

[38] X. Miao, X. Yuan, Y. Pu, V. Athitsos. Lambda-net: reconstruct hyperspectral images from a snapshot measurement. IEEE/CVF International Conference on Computer Vision (ICCV), 4058-4068(2019).

[39] J. M. Bioucas-Dias, M. A. Figueiredo. A new TwIST: two-step iterative shrinkage/thresholding algorithms for image restoration. IEEE Trans. Image Process., 16, 2992-3004(2007).

[40] E. J. Candes, T. Tao. Near-optimal signal recovery from random projections: universal encoding strategies?. IEEE Trans. Inform. Theory, 52, 5406-5425(2006).

[41] E. J. Candes, J. K. Romberg, T. Tao. Stable signal recovery from incomplete and inaccurate measurements. Commun. Pure Appl. Math., 59, 1207-1223(2006).

[42] X. Liu, X. Wang. Fourth-order tensors with multidimensional discrete transforms(2017).

[43] J. Barzilai, J. M. Borwein. Two-point step size gradient methods. IMA J. Numer. Anal., 8, 141-148(1988).

[44] M. Raydan. Convergence properties of the Barzilai and Borwein gradient method(1991).

[45] B. Lim, S. Son, H. Kim, S. Nah, K. Mu Lee. Enhanced deep residual networks for single image super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 136-144(2017).

[46] L. Yue, X. Miao, P. Wang, B. Zhang, X. Zhen, X. Cao. Attentional alignment networks. 29th British Machine Vision Conference, 1-14(2018).

[47] S. Min, X. Chen, Z. Zha, F. Wu, Y. Zhang. A two-stream mutual attention network for semi-supervised biomedical segmentation with noisy labels. Proceedings of the AAAI Conference on Artificial Intelligence, 4578-4585(2019).

[48] Y. Li, Z. Xiao, X. Zhen, X. Cao. Attentional information fusion networks for cross-scene power line detection. IEEE Geosci. Remote Sens. Lett., 16, 1635-1639(2019).

[49] Y. Huang, X. Cao, X. Zhen, J. Han. Attentive temporal pyramid network for dynamic scene classification. Proceedings of the AAAI Conference on Artificial Intelligence, 8497-8504(2019).

[50] D. P. Kingma, J. Ba. Adam: a method for stochastic optimization(2014).

[51] S. H. Chan, R. Khoshabeh, K. B. Gibson, P. E. Gill, T. Q. Nguyen. An augmented Lagrangian method for video restoration. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 941-944(2011).

[52] https://drive.google.com/file/d/1PGav_1Yju2lfXlXQsxnvkMPu_j_EWm0B/view. https://drive.google.com/file/d/1PGav_1Yju2lfXlXQsxnvkMPu_j_EWm0B/view

[53] https://www.pexels.com/video/blue-jellyfish-856882/. https://www.pexels.com/video/blue-jellyfish-856882/

[54] https://www.pexels.com/video/a-person-using-a-smart-cellular-phone-3143531/. https://www.pexels.com/video/a-person-using-a-smart-cellular-phone-3143531/

[55] H. Yu, S. Winkler. Image complexity and spatial information. Fifth International Workshop on Quality of Multimedia Experience (QoMEX), 12-17(2013).

[56] C. Yang, D. Qi, F. Cao, Y. He, J. Yao, P. Ding, X. Ouyang, Y. Yu, T. Jia, S. Xu. Single-shot receive-only ultrafast electro-optical deflection imaging. Phys. Rev. Appl., 13, 024001(2020).

[57] C. Yang, F. Cao, D. Qi, Y. He, P. Ding, J. Yao, T. Jia, Z. Sun, S. Zhang. Hyperspectrally compressed ultrafast photography. Phys. Rev. Lett., 124, 023902(2020).

[58] M. Iliadis, L. Spinoulas, A. K. Katsaggelos. Deep fully-connected networks for video compressive sensing. Digit. Signal Process., 72, 9-18(2018).

[59] K. Kulkarni, S. Lohit, P. Turaga, R. Kerviche, A. Ashok. ReconNet: non-iterative reconstruction of images from compressively sensed measurements. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 449-458(2016).

[60] H. Yao, F. Dai, S. Zhang, Y. Zhang, Q. Tian, C. Xu. DR²-Net: deep residual reconstruction network for image compressive sensing. Neurocomputing, 359, 483-493(2019).

[61] D. Gedalin, Y. Oiknine, A. Stern. DeepCubeNet: reconstruction of spectrally compressive sensed hyperspectral images with deep neural networks. Opt. Express, 27, 35811-35822(2019).

[62] J. Nocedal, S. Wright. Numerical Optimization(2006).