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像元映射变分辨率光谱成像重构

肖树林 胡长虹 高路尧 颜克雄 杨春吉 李洪利

肖树林, 胡长虹, 高路尧, 颜克雄, 杨春吉, 李洪利. 像元映射变分辨率光谱成像重构[J]. 中国光学(中英文), 2022, 15(5): 1045-1054. doi: 10.37188/CO.2022-0108
引用本文: 肖树林, 胡长虹, 高路尧, 颜克雄, 杨春吉, 李洪利. 像元映射变分辨率光谱成像重构[J]. 中国光学(中英文), 2022, 15(5): 1045-1054. doi: 10.37188/CO.2022-0108
XIAO Shu-lin, HU Chang-hong, GAO Lu-yao, YAN Ke-xiong, YANG Chun-ji, LI Hong-li. Pixel mapping variable-resolution spectral imaging reconstruction[J]. Chinese Optics, 2022, 15(5): 1045-1054. doi: 10.37188/CO.2022-0108
Citation: XIAO Shu-lin, HU Chang-hong, GAO Lu-yao, YAN Ke-xiong, YANG Chun-ji, LI Hong-li. Pixel mapping variable-resolution spectral imaging reconstruction[J]. Chinese Optics, 2022, 15(5): 1045-1054. doi: 10.37188/CO.2022-0108

像元映射变分辨率光谱成像重构

doi: 10.37188/CO.2022-0108
基金项目: 吉林省与中国科学院科技合作高技术产业化(No. 2020SYHZ0028);(吉林省) 2021年省预算内基本建设资金(No. 2021C045-3)。
详细信息
    作者简介:

    肖树林(1996—),男,江西赣州人,硕士研究生,2020年于南昌航空大学获得学士学位,主要从事智能图像处理、计算光谱成像方面的研究。E-mail:13263073168@163.com

    胡长虹(1982—),男,吉林长春人,副研究员,博士生导师,2013年于吉林大学获得博士学位,2012—2013年在美国西弗吉尼亚大学做访问学者,主要从事高光谱成像、计算成像、数据挖掘、软件质量评价方面的研究。E-mail:changhonghu@rocketmail.com

  • 中图分类号: O438

Pixel mapping variable-resolution spectral imaging reconstruction

Funds: This research is funded by the cooperation project between Jilin Province and Chinese Academy of Sciences (No. 2020SYHZ0028); (Jilin Province) Capital construction funds within the provincial budget in 2021 (No. 2021C045-3).
More Information
  • 摘要:

    本文讨论了随机滤光片光谱编码-解码的基本原理与重构方法,利用深度学习欠完备自编码器的自动特征提取机制,构建了高精度、低延时的像元映射变分辨率光谱成像重构网络,通过变换像元映射关系完成了2×2、4×4像元阵列光谱重构网络的并行训练。最后,利用512×512、120谱段(430 ~670 nm)的遥感光谱图像对重构网络进行验证,实现了2×2像元阵列/40谱段重构峰值信噪比达53 dB、均方误差小于0.002、重构用时0.87 s与4×4像元阵列/120谱段重构峰值信噪比达64 dB、均方误差小于10−5、重构用时0.52 s的变分辨率光谱图像重构。实验结果表明像元映射变分辨率光谱成像重构网络具备高精度、低延时的动态变换性能。

     

  • 图 1  压缩感知光谱编解码原理

    Figure 1.  Principle diagram of compressed sensing spectral encoding-decoding

    图 2  深度学习光谱编解码原理

    Figure 2.  Principle diagram of deep learning spectral encoding-decoding

    图 3  随机滤光片与光谱重构网络协同设计

    Figure 3.  Collaborative design of random filter and spectral reconstruction network

    图 4  像元映射变分辨率光谱重构网络训练

    Figure 4.  Spectral reconstruction network training of pixel mapping variable resolution

    图 5  像元级随机滤光片光谱成像

    Figure 5.  Pixel random filter spectral imaging

    图 6  像元随机滤光片变分辨率动态转换示意

    Figure 6.  Variable resolution dynamic conversion of a pixel random filter

    图 7  训练与验证损失曲线

    Figure 7.  Training and verification loss curve

    图 8  随机滤光片透过率曲线

    Figure 8.  Transmission curves of random filter

    图 9  样本外预测结果

    Figure 9.  Out-sample forecasting results

    图 10  遥感高光谱图像验证结果Fig.10 Remote sensing hyperspectral image verification results

  • [1] LOBO J, DIAS J. Vision and inertial sensor cooperation using gravity as a vertical reference[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(12): 1597-1608. doi: 10.1109/TPAMI.2003.1251152
    [2] CORKE P, LOBO J, DIAS J. An introduction to inertial and visual sensing[J]. The International Journal of Robotics Research, 2007, 26(6): 519-535. doi: 10.1177/0278364907079279
    [3] HAGEN N A, KUDENOV M W. Review of snapshot spectral imaging technologies[J]. Optical Engineering, 2013, 52(9): 090901. doi: 10.1117/1.OE.52.9.090901
    [4] YANG Z Y, ALBROW-OWEN T, CAI W W, et al. Miniaturization of optical spectrometers[J]. Science, 2021, 371(6528): eabe0722. doi: 10.1126/science.abe0722
    [5] 左超, 陈钱. 计算光学成像: 何来, 何处, 何去, 何从?[J]. 红外与激光工程,2022,51(2):20220110. doi: 10.3788/IRLA20220110

    ZUO CH, CHEN Q. Computational optical imaging: an overview[J]. Infrared and Laser Engineering, 2022, 51(2): 20220110. (in Chinese) doi: 10.3788/IRLA20220110
    [6] ZHU X X, BIAN L H, FU H, et al. Broadband perovskite quantum dot spectrometer beyond human visual resolution[J]. Light:Science &Applications, 2020, 9: 73.
    [7] YANG Z Y, ALBROW-OWEN T, CUI H X, et al. Single-nanowire spectrometers[J]. Science, 2019, 365(6457): 1017-1020. doi: 10.1126/science.aax8814
    [8] WANG ZH, YI S, CHEN A, et al. Single-shot on-chip spectral sensors based on photonic crystal slabs[J]. Nature Communications, 2019, 10(1): 1020. doi: 10.1038/s41467-019-08994-5
    [9] LEE W B, OLIVER J, KIM S C, et al. . Random optical scatter filters for spectrometers: implementation and estimation[C]. Propagation Through and Characterization of Distributed Volume Turbulence 2013, Optical Society of America, 2013: JTu4A. 33.
    [10] XIONG J, CAI X SH, CUI K Y, et al. Dynamic brain spectrum acquired by a real-time ultraspectral imaging chip with reconfigurable metasurfaces[J]. Optica, 2022, 9(5): 461-468. doi: 10.1364/OPTICA.440013
    [11] ZHANG W Y, SONG H Y, HE X, et al. Deeply learned broadband encoding stochastic hyperspectral imaging[J]. Light:Science &Applications, 2021, 10: 108.
    [12] DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306. doi: 10.1109/TIT.2006.871582
    [13] OLIVER J, LEE W, PARK S, et al. Improving resolution of miniature spectrometers by exploiting sparse nature of signals[J]. Optics Express, 2012, 20(3): 2613-2625. doi: 10.1364/OE.20.002613
    [14] BARANIUK R, DAVENPORT M, DEVORE R, et al. A simple proof of the restricted isometry property for random matrices[J]. Constructive Approximation, 2008, 28(3): 253-263. doi: 10.1007/s00365-007-9003-x
    [15] CANDÈS E J. Compressive sampling[C]. Proceedings of the International Congress of Mathematicians, 2006: 1-20.
    [16] HUANG L Q, LUO R CH, LIU X, et al. Spectral imaging with deep learning[J]. Light:Science &Applications, 2022, 11: 61.
    [17] 王雅思, 姚鸿勋, 孙晓帅, 等. 深度学习中的自编码器的表达能力研究[J]. 计算机科学,2015,42(9):56-60,65. doi: 10.11896/j.issn.1002-137X.2015.09.012

    WANG Y S, YAO H X, SUN X SH, et al. Representation ability research of auto-encoders in deep learning[J]. Computer Science, 2015, 42(9): 56-60,65. (in Chinese) doi: 10.11896/j.issn.1002-137X.2015.09.012
    [18] 冯驰, 常军, 杨海波. 双小凹光学成像系统设计[J]. 物理学报,2015,64(3):034201. doi: 10.7498/aps.64.034201

    FENG CH, CHANG J, YANG H B. Design of dually foveated imaging optical system[J]. Acta Physica Sinica, 2015, 64(3): 034201. (in Chinese) doi: 10.7498/aps.64.034201
    [19] CHENG Y, CAO J, HAO Q, et al. Compound eye and retina-like combination sensor with a large field of view based on a space-variant curved micro lens array[J]. Applied Optics, 2017, 56(12): 3502-3509. doi: 10.1364/AO.56.003502
    [20] ARAD, BEN-SHAHAR, et al.. Sparse recovery of hyperspectral signal from natural RGB images[C]. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands, October 11–14, 2016, 19–34. Springer.
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出版历程
  • 收稿日期:  2022-05-30
  • 修回日期:  2022-06-22
  • 网络出版日期:  2022-08-03

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