Terahertz image super-resolution based on a complex convolutional neural network | |
Wang Y(王莹)1,3,4,5; Qi F(祁峰)1,2,4,5; Wang JK(汪晋宽)3 | |
刊名 | Optics Letters |
2021 | |
卷号 | 46期号:13页码:3123-3126 |
ISSN号 | 0146-9592 |
产权排序 | 1 |
英文摘要 | Terahertz (THz) imaging has been applied successfully in numerous applications, from medical imaging to industrial non-destructive detection. However, low resolution has always been a problem due to its long wavelength. A convolution neural network (CNN) is quite effective at improving the resolution of images in optics, in which real numbers are manipulated corresponding to measured intensity. Compared to optics, it is quite feasible to gain both the amplitude and phase information in THz imaging. In this Letter, we have extended the CNN from a real number domain to a complex number domain based on the wave nature of THz light. To the best of our knowledge, this is the first time that such a complex convolution neural network (CCNN) has been shown to be successful in THz imaging. We have proved that resolution can be 0.4 times of the beam size via this approach, and half a wavelength resolution can be obtained easily. Compared to the CNN, the CCNN generates an extra 27.8% increase in terms of contrast, implying a better image. Phase information can be recovered well, which is impossible for the CNN. Although the network is trained by the MNIST dataset, it is quite powerful for image reconstruction. Again, the CCNN outperforms the CNN in terms of generalization capability. We believe such an approach can help to overcome the lower-resolution bottleneck in THz imaging, and it can release the requirement of critical optical components and extensive fine-tuning in systems. THz biomedical imaging, non-destructive testing (NDT), and a lot of imaging applications can benefit from this approach. |
资助项目 | Research Institute of Robotics and Intelligent Manufacturing Innovation, Chinese Academy of Sciences[C2019001] |
WOS研究方向 | Optics |
语种 | 英语 |
WOS记录号 | WOS:000668963500032 |
资助机构 | Research Institute of Robotics and Intelligent Manufacturing Innovation, Chinese Academy of Sciences (C2019001). |
内容类型 | 期刊论文 |
源URL | [http://ir.sia.cn/handle/173321/29195] |
专题 | 沈阳自动化研究所_光电信息技术研究室 |
通讯作者 | Qi F(祁峰) |
作者单位 | 1.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China 2.University of Chinese Academy of Sciences, Beijing, 100049, China 3.School of Communication Science and Engineering, Northeastern University, Liaoning Province, Shenyang, 110819, China 4.Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang, 110016, China 5.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China |
推荐引用方式 GB/T 7714 | Wang Y,Qi F,Wang JK. Terahertz image super-resolution based on a complex convolutional neural network[J]. Optics Letters,2021,46(13):3123-3126. |
APA | Wang Y,Qi F,&Wang JK.(2021).Terahertz image super-resolution based on a complex convolutional neural network.Optics Letters,46(13),3123-3126. |
MLA | Wang Y,et al."Terahertz image super-resolution based on a complex convolutional neural network".Optics Letters 46.13(2021):3123-3126. |
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