| Citation: | WANG Lu-yang, LIANG Jing-qiu, ZHAO Bai-xuan, NIE Hai-tao, CHEN Yu-peng, ZHAO Ying-ze, ZHENG Kai-feng, QIN Yu-xin, WANG Wei-biao, LIU Yu, LI Zi-zheng, LV Jin-guang. Physics-driven mid-wave infrared spectral compressed encoding and reconstruction[J]. Chinese Optics. doi: 10.37188/CO.2026-0015
|
Aiming at the problem that existing spectral compressed sensing algorithms adapted to the visible band are difficult to achieve high-precision reconstruction for sharp gas absorption features in the mid-wave infrared (MWIR) spectra, this paper proposes a physics-driven MWIR spectral compressed encoding and reconstruction network to realize high-precision reconstruction of MWIR spectra with sharp gas absorption features. The dual-branch MWIR spectral reconstruction network serves as the core module of the proposed framework. Specifically, the network consists of two parallel branches, namely the smooth background reconstruction branch and the characteristic absorption reconstruction branch, which respectively realize the accurate reconstruction of smooth background logarithmic spectrum and sharp gas characteristic absorbance. Subsequently, high-accuracy reconstruction of MWIR gas absorption spectra is achieved through information fusion, physical quantity conversion, and post-processing with fully connected layers. Experimental results on the reconstruction of gas absorption spectra within the 3.7−4.8 μm band with 45 channels in real-world scenarios demonstrate that the proposed method achieves a peak signal-to-noise ratio (PSNR) of more than 28.159 dB and a spectral angle mapper (SAM) value of better than 0.053 rad. For a data cube with an image resolution of 320×256, the reconstruction time is approximately 0.65 seconds. This method effectively breaks through the technical bottleneck of high-precision MWIR spectral reconstruction, and it features both the interpretability of physics-driven models and the generalization capability of data-driven models. It provides a feasible technical path for MWIR spectral compressed sensing and exhibits significant potential for practical applications.
| [1] |
XING CH ZH, WEI SH C, LI Y K, et al. Fast-hyperspectral imaging remote sensing: emission quantification of NO2 and SO2 from marine vessels[J]. Light: Science & Applications, 2025, 14(1): 308.
|
| [2] |
KASTEK M, SOSNOWSKI T, ORŻANOWSKI T, et al. Multispectral gas detection method[C]. WIT Transactions on Ecology and the Environment: Vol. 123, WIT Press, 2009: 227-236.
|
| [3] |
MERIBOUT M. Gas leak-detection and measurement systems: prospects and future trends[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 4505813.
|
| [4] |
CHEN Y P, LIANG J Q, ZHAO B X, et al. Snapshot infrared Fourier transform imaging spectrometer for transient dynamic sensing[J]. Optics Express, 2025, 33(3): 4024-4043. doi: 10.1364/OE.550328
|
| [5] |
吕金光, 梁静秋, 赵百轩, 等. 全景双谱段红外成像干涉光谱测量反演仪器[J]. 中国光学(中英文), 2022, 15(5): 1092-1104.
LV J G, LIANG J Q, ZHAO B X, et al. Panoramic bispectral infrared imaging interference spectrum measurement inversion instrument[J]. Chinese Optics, 2022, 15(5): 1092-1104. (in Chinese).
|
| [6] |
YANG SH W, QIN H L, YAN X, et al. Mid-wave infrared snapshot compressive spectral imager with deep infrared denoising prior[J]. Remote Sensing, 2023, 15(1): 280. doi: 10.3390/rs15010280
|
| [7] |
YUAN L Y, WEN M X, WANG Y M, et al. Development and evaluation of MWIR imaging spectrometer for multi-dimensional detection[J]. Infrared Physics & Technology, 2024, 137: 105148. doi: 10.1016/j.infrared.2024.105148
|
| [8] |
孔相金, 李博, 李寒霜, 等. 痕量气体掩星探测高光谱成像光谱仪光学系统设计[J]. 中国光学(中英文), 2024, 17(3): 661-673.
KONG X J, LI B, LI H SH, et al. Optical system design of hyperspectral imaging spectrometer for trace gas occultation detection[J]. Chinese Optics, 2024, 17(3): 661-673. (in Chinese).
|
| [9] |
ZHANG W Y, SONG H Y, HE X, et al. Deeply learned broadband encoding stochastic hyperspectral imaging[J]. Light: Science & Applications, 2021, 10(1): 108.
|
| [10] |
SONG H Y, MA Y G, HAN Y B, et al. Deep-learned broadband encoding stochastic filters for computational spectroscopic instruments[J]. Advanced Theory and Simulations, 2021, 4(3): 2000299. doi: 10.1002/adts.202000299
|
| [11] |
WU N F, SUN Y X, HU J T, et al. Intelligent nanophotonics: when machine learning sheds light[J]. eLight, 2025, 5(1): 5. doi: 10.1186/s43593-025-00085-x
|
| [12] |
DREMEL J, SCHARF E, RICHTER S, et al. Lensless single-shot multicore fiber endomicroscopy using a single multispectral hologram[J]. Light: Advanced Manufacturing, 2025, 6(4): 27. doi: 10.37188/lam.2025.027
|
| [13] |
杨文庆, 霍茨, 孟贺岩, 等. 宽带光谱结构编码计算光谱成像研究进展[J]. 红外与激光工程, 2025, 54(7): 202501. doi: 10.3788/IRLA20250130
YANG W Q, HUO C, MENG H Y, et al. Recent advances in computational spectral imaging based on broadband spectral structure coding[J]. Infrared and Laser Engineering, 2025, 54(7): 202501. (in Chinese). doi: 10.3788/IRLA20250130
|
| [14] |
肖树林, 胡长虹, 高路尧, 等. 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 SH L, HU CH H, GAO L Y, et al. Pixel mapping variable-resolution spectral imaging reconstruction[J]. Chinese Optics, 2022, 15(5): 1045-1054. (in Chinese). doi: 10.37188/CO.2022-0108
|
| [15] |
YAKO M, YAMAOKA Y, KIYOHARA T, et al. Video-rate hyperspectral camera based on a CMOS-compatible random array of fabry−pérot filters[J]. Nature Photonics, 2023, 17(3): 218-223. doi: 10.1038/s41566-022-01141-5
|
| [16] |
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
|
| [17] |
ARAD B, BEN-SHAHAR O. Sparse recovery of hyperspectral signal from natural RGB images[C]. Proceedings of the 14th European Conference on Computer Vision, Springer, 2016: 19-34.
|
| [18] |
徐平, 徐吉, 冯宇超, 等. 面向CASSI的高光谱图像重建算法研究综述(特邀)[J]. 光子学报, 2025, 54(9): 0954206. doi: 10.3788/gzxb20255409.0954206
XU P, XU J, FENG Y CH, et al. A review of hyperspectral image reconstruction algorithms for coded aperture snapshot spectral imaging (invited)[J]. Acta Photonica Sinica, 2025, 54(9): 0954206. (in Chinese). doi: 10.3788/gzxb20255409.0954206
|
| [19] |
宋思远, 高博, 王帅, 等. 基于光谱重构的中波红外快照型光谱成像技术研究[J]. 光子学报, 2025, 54(8): 0811001. doi: 10.3788/gzxb20255408.0811001
SONG S Y, GAO B, WANG SH, et al. Mid-wave infrared snapshot spectral imaging via spectral reconstruction[J]. Acta Photonica Sinica, 2025, 54(8): 0811001. (in Chinese). doi: 10.3788/gzxb20255408.0811001
|
| [20] |
HUANG L Q, LUO R CH, LIU X, et al. Spectral imaging with deep learning[J]. Light: Science & Applications, 2022, 11(1): 61.
|
| [21] |
FENG SH Q, WANG ZH SH, CHENG X B, et al. Superposition Fabry−Perot filter array for a computational hyperspectral camera[J]. Optics Letters, 2023, 48(5): 1156-1159. doi: 10.1364/OL.479622
|
| [22] |
YANG J W, CUI K Y, CAI X SH, et al. Ultraspectral imaging based on metasurfaces with freeform shaped meta-atoms[J]. Laser & Photonics Reviews, 2022, 16(7): 2100663. doi: 10.1002/lpor.202100663
|
| [23] |
YANG J W, CUI K Y, HUANG Y D, et al. Deep-learning based on-chip rapid spectral imaging with high spatial resolution[J]. Chip, 2023, 2(2): 100045. doi: 10.1016/j.chip.2023.100045
|
| [24] |
BIAN L H, WANG ZH, ZHANG Y ZH, et al. A broadband hyperspectral image sensor with high spatio-temporal resolution[J]. Nature, 2024, 635(8037): 73-81. doi: 10.1038/s41586-024-08109-1
|
| [25] |
ZHU J P, NIE H T, WANG L Y, et al. Snapshot hyperspectral imaging with high spatial resolution based on transformers[J]. Optics Express, 2025, 33(15): 31731-31755. doi: 10.1364/OE.563614
|
| [26] |
张颖, 连彦喆, 张晞, 等. 基于DD-CASSI退化成像模型的高光谱图像重建算法(内封面文章·特邀)[J]. 红外与激光工程, 2026, 55(2): 20250611. doi: 10.3788/IRLA20250611
ZHANG Y, LIAN Y ZH, ZHANG X, et al. Hyperspectral image reconstruction algorithm based on the DD-CASSI degradation imaging model (inner cover paper·invited)[J]. Infrared and Laser Engineering, 2026, 55(2): 20250611. (in Chinese). doi: 10.3788/IRLA20250611
|
| [27] |
RÜDINGER A, ATMACA Ö, BAHLINGER V, et al. Bimodal tissue differentiation using hyperspectral imaging and elastographic Fourier transform profilometry[J]. Light: Advanced Manufacturing, 2025, 6(4): 73. doi: 10.37188/lam.2025.073
|
| [28] |
HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]. Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2018: 7132-7141.
|
| [29] |
WANG Q L, WU B G, ZHU P F, et al. ECA-net: efficient channel attention for deep convolutional neural networks[C]. Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2020: 11531-11539.
|
| [30] |
ROTHMAN L, TASHKUN S, MIKHAILENKO S, et al. HITRAN on the web[EB/OL]. [2026-01-26]. https://hitran.iao.ru.
|
Figures(11) / Tables(7)
DownLoad:
