Dual-domain Multiscale Feature Merging-Based Multiresolution Temperature Tomography for Laser Absorption Spectroscopy

Jingjing Si; Jingbo Wang; Yinbo Cheng; Chang Liu

doi:10.3788/CJL240684

Chinese Journal of Lasers, Volume. 51, Issue 23, 2311002(2024)

Dual-domain Multiscale Feature Merging-Based Multiresolution Temperature Tomography for Laser Absorption Spectroscopy

Jingjing Si^1,4, Jingbo Wang¹, Yinbo Cheng^2、*, and Chang Liu³

Author Affiliations

¹School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, Hebei , China

²Ocean College, Hebei Agricultural University, Qinhuangdao 066003, Hebei , China

³School of Engineering, the University of Edinburgh, Edinburgh EH93JL, UK

⁴Hebei Key Laboratory of Information Transmission and Signal Processing, Qinhuangdao 066004, Hebei , China

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Figures & Tables(13)

Fig. 1. Schematic of multi-resolution spatial discretization model for RoI. (a) Low-resolution discretization; (b) high-resolution discretization

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Fig. 2. Overall architecture of DMFMMnet

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Fig. 3. Structure of MMCM

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Fig. 4. Structure of AMFMM

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Fig. 5. Structure of FEM

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Fig. 6. Laser beam arrangement of experimental TDLAS sensor

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Fig. 7. Comparison of low-resolution temperature distribution images reconstructed by three networks. (a) Sample image of low-resolution temperature distribution; low-resolution temperature distribution images reconstructed by (b) H-CNN-LR, (c) HVTMFnet-LR, and (d) DMFMMnet

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Fig. 8. Comparison of high-resolution temperature distribution images reconstructed by six networks. (a) Sample image of high-resolution temperature distribution; high-resolution temperature distribution images reconstructed by (b) H-CNN-HR, (c) HVTMFnet-HR, (d) LBlock+Bicubic, (e) LBlock+SRCNN, (f) LBlock+ESRT, and (g) DMFMMnet

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Fig. 9. Temperature distribution images reconstructed using real measurement data of TDLAS sensor. (a) Top view of measurement field of TDLAS sensor; low-resolution temperature distribution images reconstructed by (b) DMFMMnet, (c) HVTMFnet-LR, and (d) H-CNN-LR; high-resolution temperature distribution images reconstructed by (e) DMFMMnet, (f) HVTMFnet-HR, (g) H-CNN-HR, (h) LBlock+Bicubic, (i) LBlock+SRCNN, and (j) LBlock+ESRT

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Table 1. Parameter settings for each module of DMFMMnet

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Table 1. Parameter settings for each module of DMFMMnet

Module	Layer	Input dim	Stride	Output dim	Layer	Input dim	Stride	Output dim
LBlock	2×2 Conv 1	2×4×8	(1, 1)	8×3×7	FC1	520	‒	520
	2×2 Conv 2	8×3×7	(1, 1)	16×2×6	FC2	520	‒	1024
	2×2 Conv 3	16×4×8	(1, 1)	32×1×5	FC3	1024	‒	1024
	3×3 Conv 1	2×4×8	(1, 1)	16×4×8	FC4	1024	‒	1600
	3×3 Conv 2	8×3×7	(1, 1)	32×3×7	‒	‒	‒	‒
	1×1 Conv 1	32×1×5	‒	32×1×5	‒	‒	‒	‒
IMBlock	3×3 Conv 3	1×40×40	(1, 1)	32×40×40	1×1 Conv 2	16×80×1	(1, 1)	10×80×1
	3×3 Conv 4	32×40×40	(1, 1)	32×40×40	1×1 Conv 3	10×1×40	(1, 1)	16×1×40
	3×3 Conv 5	32×40×40	(1, 1)	16×40×40	1×1 Conv 4	10×1×40	(1, 1)	16×1×40
	3×3 Conv 6	16×40×40	(1, 1)	16×40×40	1×1 Conv 5	10×40×1	(1, 1)	16×40×1
	3×3 Conv 7	16×40×40	(1, 1)	32×40×40	1×1 Conv 6	10×40×1	(1, 1)	16×40×1
	5×5 Conv 1	16×40×40	(1, 1)	16×40×40	‒	‒	‒	‒
HBlock	1×1 Conv 7	32×40×40	‒	64×40×40	3×3 Conv 12	64×40×40	(1, 1)	48×40×40
	1×1 Conv 8	80×40×40	‒	64×40×40	3×3 Conv 13	48×40×40	(1, 1)	80×40×40
	3×3 Conv 8	64×40×40	(1, 1)	48×40×40	FC5	64	‒	512
	3×3 Conv 9	48×40×40	(1, 1)	32×40×40	FC6	512	‒	1600
	3×3 Conv 10	32×40×40	(1, 1)	64×40×40	Deconv	64×40×40	(2, 2)	1×80×80
	3×3 Conv 11	48×40×40	(1, 1)	64×40×40	‒	‒	‒	‒

Table 2. Comparison of mean PSNR and mean SSIM of low-resolution temperature distribution images reconstructed by three networks

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Table 2. Comparison of mean PSNR and mean SSIM of low-resolution temperature distribution images reconstructed by three networks

Network	SSIM/PSNR
Network	S_SNR=25 dB	S_SNR=30 dB	S_SNR=35 dB	S_SNR=40 dB	S_SNR=45 dB
H-CNN-LR	0.9584/23.6118	0.9721/25.1865	0.9787/26.0455	0.9778/27.2139	0.9832/27.4768
HVTMFnet-LR	0.9870/29.3102	0.9904/31.9570	0.9919/33.7668	0.9951/34.8574	0.9957/35.3281
DMFMMnet	0.9891/29.5048	0.9923/32.8666	0.9939/34.5872	0.9952/36.0022	0.9955/36.0301

Table 3. Comparison of mean PSNR and mean SSIM of high-resolution temperature distribution images reconstructed by six networks

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Table 3. Comparison of mean PSNR and mean SSIM of high-resolution temperature distribution images reconstructed by six networks

Network	SSIM/PSNR
Network	S_SNR=25 dB	S_SNR=30 dB	S_SNR=35 dB	S_SNR=40 dB	S_SNR=45 dB
H-CNN-HR	0.9677/24.1378	0.9760/25.4909	0.9810/26.6838	0.9842/27.3411	0.9854/27.9248
HVTMFnet-HR	0.9888/30.2584	0.9924/33.0355	0.9948/34.4921	0.9951/36.1158	0.9959/36.4694
LBlock+Bicubic	0.9879/29.4908	0.9931/32.7655	0.9944/34.5670	0.9951/35.4227	0.9943/35.4467
LBlock+SRCNN	0.9870/29.6452	0.9913/32.7146	0.9953/35.0641	0.9968/36.1104	0.9935/36.3672
LBlock+ESRT	0.9885/30.9144	0.9906/33.4730	0.9963/36.0816	0.9947/37.0513	0.9979/38.6104
DMFMMnet	0.9892/30.4884	0.9940/33.8099	0.9975/36.5090	0.9972/37.2751	0.9985/38.7245

Table 4. Comparison of training time and reconstruction time of 6 networks

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Table 4. Comparison of training time and reconstruction time of 6 networks

Network	Training time /s	Low-resolution temperature distribution reconstruction time /(×10^-5 s)	High-resolution temperature distribution reconstruction time /(×10^-5 s)
H-CNN-MR	153	2.036	2.774
HVTMFnet-MR	443	4.733	5.357
LBlock+Bicubic	342	‒	4.593
LBlock+SRCNN	540	‒	8.863
LBlock+ESRT	4217	‒	61.673
DMFMMnet	760	5.007	18.288

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Jingjing Si, Jingbo Wang, Yinbo Cheng, Chang Liu. Dual-domain Multiscale Feature Merging-Based Multiresolution Temperature Tomography for Laser Absorption Spectroscopy[J]. Chinese Journal of Lasers, 2024, 51(23): 2311002

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Paper Information

Category: spectroscopy

Received: Mar. 14, 2024

Accepted: May. 27, 2024

Published Online: Dec. 11, 2024

The Author Email: Yinbo Cheng (cyb@hebau.edu.cn)

DOI:10.3788/CJL240684

CSTR:32183.14.CJL240684

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology