[1] D. Le Gall. MPEG: a video compression standard for multimedia applications. Commun. ACM, 34, 46(1991).
[2] R. Puri et al. Distributed video coding in wireless sensor networks. IEEE Signal Process Mag., 23, 94(2006).
[3] W. Shi et al. Edge computing: vision and challenges. IEEE Internet Things J., 3, 637(2016).
[4] S. H. Chan, X. Wang, O. A. Elgendy. Plug-and-play ADMM for image restoration: fixed-point convergence and applications. IEEE Trans. Comput. Imaging, 3, 84(2017).
[5] E. K. Ryu et al. Plug and-play methods provably converge with properly trained denoisers, 5546(2019).
[6] S. V. Venkatakrishnan, C. A. Bouman, B. Wohlberg. Plug-and-play priors for model based reconstruction, 945(2013).
[7] S. Sreehari et al. Plug-and-play priors for bright field electron tomography and sparse interpolation. IEEE Trans. Comput. Imaging, 2, 408(2016).
[8] X. Yuan et al. Plug-and-play algorithms for large-scale snapshot compressive imaging, 1447(2020).
[9] E. J. Candes, J. Romberg, T. Tao. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inf. Theory, 52, 489(2006).
[10] D. L. Donoho. Compressed sensing. IEEE Trans. Inf. Theory, 52, 1289(2006).
[11] X. Yuan, D. J. Brady, A. K. Katsaggelos. Snapshot compressive imaging: theory, algorithms, and applications. IEEE Signal Process Mag., 38, 65(2021).
[12] P. Llull et al. Coded aperture compressive temporal imaging. Opt. Express, 21, 10526(2013).
[13] X. Yuan et al. Low-cost compressive sensing for color video and depth, 3318(2014).
[14] M. Qiao et al. Deep learning for video compressive sensing. APL Photonics, 5, 030801(2020).
[15] A. Wagadarikar et al. Single disperser design for coded aperture snapshot spectral imaging. Appl. Opt., 47, B44(2008).
[16] Z. Meng, J. Ma, X. Yuan. End-to-end low cost compressive spectral imaging with spatial-spectral self-attention, 187(2020).
[17] M. Qiao, X. Liu, X. Yuan. Snapshot spatial-temporal compressive imaging. Opt. Lett., 45, 1659(2020).
[18] P. Llull et al. Image translation for single shot focal tomography. Optica, 2, 822(2015).
[19] T.-H. Tsai et al. Spectral-temporal compressive imaging. Opt. Lett., 40, 4054(2015).
[20] Y. Sun, X. Yuan, S. Pang. High-speed compressive range imaging based on active illumination. Opt. Express, 24, 22836(2016).
[21] M. Qiao et al. Snapshot coherence tomographic imaging. IEEE Trans. Comput. Imaging, 7, 624(2021).
[22] Y. Sun, X. Yuan, S. Pang. Compressive high-speed stereo imaging. Opt. Express, 25, 18182(2017).
[23] M. F. Duarte et al. Single-pixel imaging via compressive sampling. IEEE Signal Process Mag., 25, 83(2008).
[24] A. Buades, B. Coll, J.-M. Morel. A non-local algorithm for image denoising, 60(2005).
[25] K. Dabov et al. Image denoising with block-matching and 3D filtering. Proc. SPIE, 6064, 606414(2006).
[26] Y. Lai et al. Single-shot ultraviolet compressed ultrafast photography. Laser Photonics Rev., 14, 2000122(2020).
[27] J. Bioucas-Dias, M. Figueiredo. A new TwIST: two-step iterative shrinkage/thresholding algorithms for image restoration. IEEE Trans. Image Process., 16, 2992(2007).
[28] X. Yuan. Generalized alternating projection based total variation minimization for compressive sensing, 2539(2016).
[29] Y. Liu et al. Rank minimization for snapshot compressive imaging. IEEE Trans. Pattern Anal. Mach. Intell., 41, 2990(2019).
[30] X. Miao et al. λ-net: Reconstruct hyperspectral images from a snapshot measurement, 4059(2019).
[31] Z. Cheng et al. Memory-efficient network for large-scale video compressive sensing, 16246(2021).
[32] Y. Xue et al. Reliable deep-learning-based phase imaging with uncertainty quantification. Optica, 6, 618(2019).
[33] Y. Li, Y. Xue, L. Tian. Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media. Optica, 5, 1181(2018).
[34] J. Ma et al. Deep tensor ADMM-Net for snapshot compressive imaging, 10223(2019).
[35] L. Wang et al. Hyperspectral image reconstruction using a deep spatial-spectral prior, 8024(2019).
[36] Y. Li et al. End-to-end video compressive sensing using Anderson-accelerated unrolled networks, 1(2020).
[37] T. Huang et al. Deep Gaussian scale mixture prior for spectral compressive imaging, 16216(2021).
[38] K. Greger, Y. LeCun. Learning fast approximations of sparse coding, 399(2010).
[39] Y. Yang et al. Deep ADMM-Net for compressive sensing MRI. Advances in Neural Information Processing Systems, 29, 10(2016).
[40] C. A. Metzler, A. Mousavi, R. G. Baraniuk. Learned D-AMP: principled neural network based compressive image recovery, 1770(2017).
[41] S. Zheng et al. Deep plug-and-play priors for spectral snapshot compressive imaging. Photonics Res., 9, B18(2021).
[42] X. Yuan et al. Plug-and-play algorithms for video snapshot compressive imaging. IEEE Trans. Pattern Anal. Mach. Intell., 44, 7093(2021).
[43] K. Zhang, W. Zuo, L. Zhang. FFDNet: toward a fast and flexible solution for CNN-based image denoising. IEEE Trans. Image Process., 27, 4608(2018).
[44] M. Kellman et al. Memory-efficient learning for large-scale computational imaging. IEEE Trans. Comput. Imaging, 6, 1403(2020).
[45] S. Jalali, X. Yuan. Snapshot compressed sensing: performance bounds and algorithms. IEEE Trans. Inf. Theory, 65, 8005(2019).
[46] X. Liao, H. Li, L. Carin. Generalized alternating projection for weighted-2.1 minimization with applications to model-based compressive sensing. SIAM J. Imag. Sci., 7, 797(2014).
[47] J. R. Hershey, J. L. Roux, F. Weninger. Deep unfolding: model-based inspiration of novel deep architectures(2014).
[48] O. Ronneberger, P. Fischer, T. Brox. U-net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention (MICCAI), 9351, 234(2015).
[49] S. Nah, T. Hyun Kim, K. Mu Lee. Deep multi-scale convolutional neural network for dynamic scene deblurring, 3883(2017).
[50] F. Perazzi et al. A benchmark dataset and evaluation methodology for video object segmentation, 724(2016).
[51] Z. Wang et al. Image quality assessment: From error visibility to structural similarity. IEEE Trans. Image Process., 13, 600(2004).
[52] A. Mercat, M. Viitanen, J. Vanne. UVG dataset: 50/120 fps 4k sequences for video codec analysis and development, 297(2020).
[53] Z. Zha et al. Image restoration via reconciliation of group sparsity and low-rank models,”. IEEE Trans. Image Process., 30, 5223(2021).
[54] X. Yuan, R. Haimi-Cohen. Image compression based on compressive sensing: End-to-end comparison with jpeg. IEEE Trans. Multimedia, 22, 2889(2020).
[55] J. Zhang, B. Ghanem. ISTA-Net: interpretable optimization-inspired deep network for image compressive sensing, 1828(2018).
[56] X. Yuan, Y. Pu. Parallel lensless compressive imaging via deep convolutional neural networks. Opt. Express, 26, 1962(2018).
[57] Lu Gan. Block compressed sensing of natural images, 403(2007).
[58] D. You et al. Coast: controllable arbitrary-sampling network for compressive sensing. IEEE Trans. Image Process., 30, 6066(2021).
[59] Z. Zha et al. Triply complementary priors for image restoration. IEEE Trans. Image Process., 30, 5819(2021).
