Laser Journal, Volume. 45, Issue 12, 1(2024)

The review of snapshot hyperspectral imaging technology based on coded compression

XIE Hui1, DUAN Meng1, WU Wei1, ZHANG Yunqiang1, PAN Guoqing1, WANG Weiqiang1, and MU Shibo2
Author Affiliations
  • 1China airborne missile academy, Luoyang Henan 471009, China
  • 2The First Military Representative Office of Air Force Equipment Department in Luoyang, Luoyang Henan 471009, China
  • show less
    References(53)

    [1] [1] Proakis J G. Digital signal processing: principles, algorithms, and applications[M]. Pearson Education India, 2007.

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

    [3] [3] 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.

    [4] [4] Gehm M E, John R, Brady D J, et al. Single-shot compressive spectral imaging with a dual-disperser architecture[J]. Optics Express, 2007, 15(21): 14013-14027.

    [5] [5] 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.

    [6] [6] Xie H, Zhao Z, Han J, et al. Dual camera snapshot highresolution-hyperspectral imaging system with parallel joint optimization via physics-informed learning[J]. Optics Express, 2023, 31(9): 14617-14639.

    [7] [7] Edgar M P, Gibson G M, Padgett M J. Principles and prospects for single-pixel imaging[J]. Nature Photonics, 2019, 13(1): 13-20.

    [8] [8] Bioucas-dias J M, Figueiredo M A T. A new TwIST: Twostep iterative shrinkage/ thresholding algorithms for image restoration[J]. IEEE Transactions on Image processing, 2007, 16(12): 2992-3004.

    [9] [9] Yang J, Liao X, Yuan X, et al. Compressive sensing by learning a Gaussian mixture model from measurements[J]. IEEE Transactions on Image Processing, 2014, 24(1): 106-119.

    [10] [10] Yuan X, Brady D J, Katsaggelos A K. Snapshot compressive imaging: Theory, algorithms, and applications[J]. IEEE Signal Processing Magazine, 2021, 38(2): 65-88.

    [11] [11] Liu Y, Yuan X, Suo J, et al. Rank minimization for snapshot compressive imaging[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 41(12): 2990-3006.

    [12] [12] Yuan X. Generalized alternating projection based total variation minimization for compressive sensing[C]//2016 IEEE International Conference on Image Processing (ICIP). IEEE, 2016: 2539-2543.

    [13] [13] Boyd S, Parikh N, Chu E, et al. Distributed optimization and statistical learning via the alternating direction method of multipliers[J]. Foundations and Trends in Machine learning, 2011, 3(1): 1-122.

    [14] [14] Toi I, Frossard P. Dictionary learning[J]. IEEE Signal Processing Magazine, 2011, 28(2): 27-38.

    [15] [15] Wang L, Wu Z, Zhong Y, et al. Snapshot spectral compressive imaging reconstruction using convolution and contextual Transformer[J]. Photonics Research, 2022, 10(8): 1848.

    [16] [16] Fan A, Xu T, Teng G, et al. Deep learning reconstruction enables full-Stokes single compression in polarized hyperspectral imaging[J]. Chinese Optics Letters, 2023, 21(5): 051101.

    [17] [17] Miao X, Yuan X, Pu Y, et al. -net: Reconstruct hyperspectral images from a snapshot measurement[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019: 4059-4069.

    [18] [18] Zhang T, Fu Y, Wang L, et al. Hyperspectral image reconstruction using deep external and internal learning[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019: 8559-8568.

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

    [20] [20] Arce G R, Rueda H, Correa C V, et al. Snapshot compressive multispectral cameras[J]. Wiley Encyclopedia of Electrical and Electronics Engineering, 1999: 1-22.

    [21] [21] Wang L, Zhang T, Fu Y, et al. Hyperreconnet: Joint coded aperture optimization and image reconstruction for compressive hyperspectral imaging[J]. IEEE Transactions on Image Processing, 2018, 28(5): 2257-2270.

    [22] [22] Courbariaux M, Bengio Y, David J P. Binaryconnect: Training deep neural networks with binary weights during propagations[J]. Advances in Neural Information Processing Systems, 2015, 28: 2187-2195.

    [23] [23] Zheng S, Liu Y, Meng Z, et al. Deep plug-and-play priors for spectral snapshot compressive imaging[J]. Photonics Research, 2021, 9(2): B18-B29.

    [24] [24] Meng Z, Jalali S, Yuan X. Gap-net for snapshot compressive imaging[J]. arXiv preprint arXiv: 2012, 08364, 2020.

    [25] [25] Meng Z, Yu Z, Xu K, et al. Self-supervised neural networks for spectral snapshot compressive imaging[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021: 2622-2631.

    [26] [26] Xie H, Zhao Z, Han J, et al. Dual camera snapshothyperspectral imaging system via physics-informed learning[J]. Optics and Lasers in Engineering, 2022, 154: 107023.

    [27] [27] Xie H, Zhao Z, Han J, et al. Dual camera snapshot highresolution-hyperspectral imaging system with parallel joint optimization via physics-informed learning[J]. Optics Express, 2023, 31(9): 14617-14639.

    [28] [28] He K, Chen X, Xie S, et al. Masked autoencoders are scalable vision learners[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022: 16000-16009.

    [29] [29] Zhao M, Chen X, Yuan X, et al. Untrained Neural Nets for Snapshot Compressive Imaging: Theory and Algorithms[J]. arXiv preprint arXiv: 2406, 03694, 2024.

    [30] [30] Song H, Ma Y, Han Y, et al. Deep-learned broadband encoding stochastic filters for computational spectroscopic instruments[J]. Advanced Theory and Simulations, 2021, 4(3): 2000299.

    [31] [31] Zhang W, Song H, He X, et al. Deeply learned broadband encoding stochastic hyperspectral imaging[J]. Light: Science & Applications, 2021, 10(1): 108.

    [32] [32] Yang J, Cui K, Cai X, et al. Ultraspectral imaging based on metasurfaces with freeform shaped meta-atoms[J]. Laser & Photonics Reviews, 2022, 16(7): 2100663.

    [33] [33] Chakrabarti A, Zickler T. Statistics of real-world hyperspectral images[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2011: 193-200.

    [34] [34] Sun Y, Zhang J, Liang R. pHSCNN: CNN-based hyperspectral recovery from a pair of RGB images[J]. Optics Express, 2022, 30(14): 24862-24873.

    [35] [35] Jia Y, Zheng Y, Gu L, et al. From RGB to spectrum for natural scenes via manifold-based mapping[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2017: 4705-4713.

    [36] [36] Tenenbaum J B, Silva V, Langford J C. A global geometric framework for nonlinear dimensionality reduction[J]. Science, 2000, 290(5500): 2319-2323.

    [37] [37] Arad B, Ben-shahar O. Sparse recovery of hyperspectral signal from natural RGB images[C]//Proceedings of the European Conference on Computer Vision (ECCV), 2016: 19-34.

    [38] [38] Sun Y, Fracchia F D, Calvert T W, et al. Deriving spectra from colors and rendering light interference[J]. IEEE Computer Graphics and Applications, 1999, 19(4): 61-67.

    [39] [39] Nguyen R M H, Prasad D K, Brown M S. Training-based spectral reconstruction from a single RGB image[C]//Proceedings of the European Conference on Computer Vision (ECCV), 2014: 186-201.

    [40] [40] Li J, Wu C, Song R, et al. Adaptive weighted attention network with camera spectral sensitivity prior for spectral reconstruction from RGB images[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020: 462-463.

    [41] [41] He T, Zhang Q, Zhou M, et al. Single-shot hyperspectral imaging based on dual attention neural network with multimodal learning[J]. Optics Express, 2022, 30(6): 9790-9813.

    [42] [42] He T, Zhang Q, Zhou M, et al. Deeply learned filter response functions for hyperspectral reconstruction[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018: 4767-4776.

    [43] [43] Antipa N, Kuo G, Heckel R, et al. DiffuserCam: lensless single-exposure 3D imaging[J]. Optica, 2018, 5(1): 1-9.

    [44] [44] Kar O F, Oktem F S. Compressive spectral imaging with diffractive lenses[J]. Optics Letters, 2019, 44(18): 4582-4585.

    [45] [45] Li L, Wang L, Song W, et al. Quantization-aware deep optics for diffractive snapshot hyperspectral imaging[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022: 19780-19789.

    [46] [46] Peng Y, Fu Q, Amata H, et al. Computational imaging using lightweight diffractive-refractive optics[J]. Optics Express, 2015, 23(24): 31393-31407.

    [47] [47] Heide F, Fu Q, Peng Y, et al. Encoded diffractive optics for full-spectrum computational imaging[J]. Scientific Reports, 2016, 6(1): 33543.

    [48] [48] Huang L, Luo R, Liu X, et al. Spectral imaging with deep learning[J]. Light: Science & Applications, 2022, 11(1): 61.

    [49] [49] Hauser J, Zeligman A, Averbuch A, et al. DD-Net: spectral imaging from a monochromatic dispersed and diffused snapshot[J]. Applied Optics, 2020, 59(36): 11196-11208.

    [50] [50] Peng Y, Fu Q, Heide F, et al. The diffractive achromat full spectrum computational imaging with diffractive optics[C]//SIGGRAPH ASIA 2016 Virtual Reality meets Physical Reality: Modelling and Simulating Virtual Humans and Environments, 2016: 1-2.

    [51] [51] Jeon D S, Baek S H, Yi S, et al. Compact snapshot hyperspectral imaging with diffracted rotation[J]. ACM Transactions on Graphics, 2015, 38(4), 1-13.

    [52] [52] Golub M A, Averbuch A, Nathan M, et al. Compressed sensing snapshot spectral imaging by a regular digital camera with an added optical diffuser[J]. Applied Optics, 2016, 55(3): 432-443.

    [53] [53] Baek S H, Ikoma H, Jeon D S, et al. Single-shot hyperspectral-depth imaging with learned diffractive optics[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021: 2651-2660.

    Tools

    Get Citation

    Copy Citation Text

    XIE Hui, DUAN Meng, WU Wei, ZHANG Yunqiang, PAN Guoqing, WANG Weiqiang, MU Shibo. The review of snapshot hyperspectral imaging technology based on coded compression[J]. Laser Journal, 2024, 45(12): 1

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category:

    Received: May. 7, 2024

    Accepted: Mar. 10, 2025

    Published Online: Mar. 10, 2025

    The Author Email:

    DOI:10.14016/j.cnki.jgzz.2024.12.001

    Topics