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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

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

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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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