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Super-resolution reconstruction algorithm for terahertz imaging below diffraction limit |
| Ying Wang(王莹)1,2,3,4, Feng Qi(祁峰)2,3,4,5,†, Zi-Xu Zhang(张子旭)6, and Jin-Kuan Wang(汪晋宽)1 |
1 School of Communication Science and Engineering, Northeastern University, Shenyang 110819, China;
2 Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China;
3 Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China;
4 Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China;
5 University of Chinese Academy of Sciences, Beijing 100049, China;
6 University of Technology Sydney, NSW 2007, Australia |
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Abstract Terahertz (THz) imaging has drawn significant attention because THz wave has a unique capability to transient, ultra-wide spectrum and low photon energy. However, the low resolution has always been a problem due to its long wavelength, limiting their application of fields practical use. In this paper, we proposed a complex one-shot super-resolution (COSSR) framework based on a complex convolution neural network to restore superior THz images at 0.35 times wavelength by extracting features directly from a reference measured sample and groundtruth without the measured PSF. Compared with real convolution neural network-based approaches and complex zero-shot super-resolution (CZSSR), COSSR delivers at least 6.67, 0.003, and 6.96% superior higher imaging efficacy in terms of peak signal to noise ratio (PSNR), mean square error (MSE), and structural similarity index measure (SSIM), respectively, for the analyzed data. Additionally, the proposed method is experimentally demonstrated to have a good generalization and to perform well on measured data. The COSSR provides a new pathway for THz imaging super-resolution (SR) reconstruction below the diffraction limit.
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Received: 13 October 2022
Revised: 25 November 2022
Accepted manuscript online: 08 December 2022
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PACS:
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87.50.U-
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87.85.dq
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(Neural networks)
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95.75.Mn
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(Image processing (including source extraction))
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| Fund: Project supported by "XingLiaoYingCai" Talents of Liaoning Province, China (Grant No. XLYC2007074), Shenyang Young and Middle-aged Science and Technology Innovation Talent Support Program (Grant No. RC200512), and the Central Guidance on Local Science and Technology Development Fund of Liaoning Province, China (Grant No. 2022JH6/100100010). |
Corresponding Authors:
Feng Qi
E-mail: qifeng@sia.cn
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Cite this article:
Ying Wang(王莹), Feng Qi(祁峰), Zi-Xu Zhang(张子旭), and Jin-Kuan Wang(汪晋宽) Super-resolution reconstruction algorithm for terahertz imaging below diffraction limit 2023 Chin. Phys. B 32 038702
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[1] Yan D X, Life J S and Wang Y 2019 Acta Phys. Sin. 68 207801 (in Chinese)
[2] Wu J Y, Xu X F and Wei L F 2020 Chin. Phys. B 29 094202
[3] Mittleman D M, Jacobsen R H and Nuss M C 1996 IEEE J. Selec. Top. Quantum Electron. 2 679
[4] Chen H, Ma S H, Yan W X, Wu X M and Wang X Z 2013 Chin. Phys. Lett. 30 030702
[5] Knobloch P, Schildknecht C, Kleine-Ostmann T, et al. 2002 Phys. Med. Biol. 47 3875
[6] Joseph C S, Yaroslavsky A N, Neel V A, Goyette T M and Giles R H 2011 Lasers in Surgery and Medicine 43 457
[7] Fan S, Ung B S, Parrott E P, Wallace V P and Pickwell-MacPherson E 2017 J. Biophoton. 10 1143
[8] Ahi K 2017 IEEE Trans. Terahertz Sci. Technol. 7 747
[9] Ahi K, Shahbazmohamadi S and Asadizanjani N 2018 Opt. Lasers Eng. 104 274
[10] Karpowicz N, Zhong H, Zhang C, Lin K I, Hwang J S, Xu J and Zhang X C 2005 Appl. Phys. Lett. 86 054105
[11] Quast H, Keil A and Löffler T 2010 35th International Conference on Infrared, Millimeter, and Terahertz Waves (IEEE) pp. 1-2
[12] Friederich F, May K H, Baccouche B, Matheis C, Bauer M, Jonuscheit J, Moor M, Denman D, Bramble J and Savage N 2018 Terahertz Radome Inspection Photonics, Vol. 5 (MDPI) p. 1
[13] Zhang Y, Tian Y, Kong Y, Zhong B and Fu Y 2018 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition pp. 2472-2481
[14] Johnson J, Alahi A and Fei-Fei L 2016 European Conference on Computer Vision (Springer) pp. 694-711
[15] Dong C, Loy C C, He K and Tang X 2015 IEEE Transactions on Pattern Analysis and Machine Intelligence 38 295
[16] Haris M, Widyanto M R and Nobuhara H 2017 Appl. Opt. 56 6043
[17] Zhang Y, Li K, Li K, Wang L, Zhong B and Fu Y 2018 Proceedings of the European Conference on Computer Vision (ECCV) pp. 286-301
[18] Haris M, Shakhnarovich G and Ukita N 2020 IEEE Transactions on Pattern Analysis and Machine Intelligence 43 4323
[19] Long Z, Wang T, You C, Yang Z, Wang K and Liu J 2019 Appl. Opt. 58 2731
[20] Li Y, Hu W, Zhang X, Xu Z, Ni J and Ligthart L P 2020 Opt. Express 28 22200
[21] Han Z, Hu W, Li Y, Xu Z, Zhao Y, Ni J and Ligthart L 2020 Cross Strait Radio Science & Wireless Technology Conference (CSRSWTC) (IEEE) pp. 1-3
[22] Ning W, Qi F, Liu Z, Wang Y, Wu H and Wang J 2019 IEEE Access 7 65116
[23] Wang Y, Qi F and Wang J 2021 Opt. Lett. 46 3123
[24] Wang Y, Qi F and Wang J 2022 Appl. Opt. 61 5831 |
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