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Super-resolution reconstruction algorithm for terahertz imaging below diffraction limit

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【作者】 王莹祁峰张子旭汪晋宽

【Author】 Ying Wang;Feng Qi;Zi-Xu Zhang;Jin-Kuan Wang;School of Communication Science and Engineering, Northeastern University;Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences;Shenyang Institute of Automation, Chinese Academy of Sciences;Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences;University of Chinese Academy of Sciences;University of Technology Sydney;

【通讯作者】 祁峰;

【机构】 School of Communication Science and Engineering, Northeastern UniversityKey Laboratory of Opto-Electronic Information Processing, Chinese Academy of SciencesShenyang Institute of Automation, Chinese Academy of SciencesInstitutes for Robotics and Intelligent Manufacturing, Chinese Academy of SciencesUniversity of Chinese Academy of SciencesUniversity of Technology Sydney

【摘要】 Terahertz(THz) imaging has drawn significant attention because THz wave has a unique capability to transient, ultrawide 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.

【Abstract】 Terahertz(THz) imaging has drawn significant attention because THz wave has a unique capability to transient, ultrawide 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.

【基金】 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);the Central Guidance on Local Science and Technology Development Fund of Liaoning Province, China (Grant No. 2022JH6/100100010)
  • 【文献出处】 Chinese Physics B ,中国物理B , 编辑部邮箱 ,2023年03期
  • 【分类号】O441.4;TP391.41
  • 【下载频次】1
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