Super-resolution reconstruction algorithm for terahertz imaging below diffraction limit

doi:10.1088/1674-1056/aca9c7

中国物理B ›› 2023, Vol. 32 ›› Issue (3): 38702-038702.doi: 10.1088/1674-1056/aca9c7

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

收稿日期:2022-10-13 修回日期:2022-11-25 接受日期:2022-12-08 出版日期:2023-02-14 发布日期:2023-02-21
通讯作者: Feng Qi E-mail:qifeng@sia.cn
基金资助:
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).

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

Received:2022-10-13 Revised:2022-11-25 Accepted:2022-12-08 Online:2023-02-14 Published:2023-02-21
Contact: Feng Qi E-mail:qifeng@sia.cn
Supported by:
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).

摘要/Abstract

摘要： 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.

关键词: terahertz, image processing, complex convolution neural network

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.

Key words: terahertz, image processing, complex convolution neural network

中图分类号:

87.50.U-

87.85.dq (Neural networks) 95.75.Mn (Image processing (including source extraction))

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[J]. 中国物理B, 2023, 32(3): 38702-038702.

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[J]. Chin. Phys. B, 2023, 32(3): 38702-038702.

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Super-resolution reconstruction algorithm for terahertz imaging below diffraction limit

Super-resolution reconstruction algorithm for terahertz imaging below diffraction limit

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