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...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.展开更多
基金"XingLiaoYingCai"Talents of Liaoning Province,China(Grant No.XLYC2007074)Shenyang Young and Middle-aged Science and Technology Innovation Talent Support Program(Grant No.RC200512)+1 种基金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),。
文摘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.
