|
1
|
Zhang Y, Lv Q, Wang J, Tang Y, Si J, Chen X, Liu Y. Spectral Super-Resolution Technology Based on Fabry-Perot Interferometer for Temporally and Spatially Modulated Fourier Transform Imaging Spectrometer. SENSORS (BASEL, SWITZERLAND) 2025; 25:1201. [PMID: 40006430 PMCID: PMC11859898 DOI: 10.3390/s25041201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2025] [Revised: 02/13/2025] [Accepted: 02/14/2025] [Indexed: 02/27/2025]
Abstract
A new spectral super-resolution technique was proposed by combining the Fabry-Perot interferometer (FPI) with Temporally and Spatially Modulated Fourier Transform Imaging Spectrometer (TSMFTIS). This study uses the multi-beam interference of the FPI to modulate the target spectrum periodically, and it acquires the modulated interferogram through TSMFTIS. The combined interference of the two techniques overcomes the limitations of the maximum optical path difference (OPD) on spectral resolution. FPI is used to encode high-frequency interference information into low-frequency interference information, proposing an inversion algorithm to recover high-frequency information, studying the impact of FPI optical defects on the system, and proposing targeted improvement algorithms. The simulation results indicate that this method can achieve multi-component joint interference imaging, improving spectral resolution by twofold. This technology offers advantages such as high throughput, stability, simple and compact structure, straightforward principles, high robustness, and low cost. It provides new insights into TSMFTIS spectral super-resolution research.
Collapse
Affiliation(s)
- Yu Zhang
- Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China; (Y.Z.); (Q.L.); (J.W.); (Y.T.); (J.S.); (X.C.)
- School of Optoelectronics, University of Chinese Academy of Sciences, No.19(A) Yuquan Road, Shijingshan District, Beijing 100049, China
- Department of Key Laboratory of Computational Optical Imagine Technology, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China
| | - Qunbo Lv
- Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China; (Y.Z.); (Q.L.); (J.W.); (Y.T.); (J.S.); (X.C.)
- School of Optoelectronics, University of Chinese Academy of Sciences, No.19(A) Yuquan Road, Shijingshan District, Beijing 100049, China
- Department of Key Laboratory of Computational Optical Imagine Technology, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China
| | - Jianwei Wang
- Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China; (Y.Z.); (Q.L.); (J.W.); (Y.T.); (J.S.); (X.C.)
- Department of Key Laboratory of Computational Optical Imagine Technology, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China
| | - Yinhui Tang
- Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China; (Y.Z.); (Q.L.); (J.W.); (Y.T.); (J.S.); (X.C.)
- Department of Key Laboratory of Computational Optical Imagine Technology, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China
| | - Jia Si
- Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China; (Y.Z.); (Q.L.); (J.W.); (Y.T.); (J.S.); (X.C.)
- Department of Key Laboratory of Computational Optical Imagine Technology, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China
| | - Xinwen Chen
- Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China; (Y.Z.); (Q.L.); (J.W.); (Y.T.); (J.S.); (X.C.)
- Department of Key Laboratory of Computational Optical Imagine Technology, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China
| | - Yangyang Liu
- Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China; (Y.Z.); (Q.L.); (J.W.); (Y.T.); (J.S.); (X.C.)
- School of Optoelectronics, University of Chinese Academy of Sciences, No.19(A) Yuquan Road, Shijingshan District, Beijing 100049, China
- Department of Key Laboratory of Computational Optical Imagine Technology, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China
| |
Collapse
|
|
2
|
Cui R, Yu H, Xu T, Xing X, Cao X, Yan K, Chen J. Deep Learning in Medical Hyperspectral Images: A Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22249790. [PMID: 36560157 PMCID: PMC9784550 DOI: 10.3390/s22249790] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 12/11/2022] [Accepted: 12/12/2022] [Indexed: 06/13/2023]
Abstract
With the continuous progress of development, deep learning has made good progress in the analysis and recognition of images, which has also triggered some researchers to explore the area of combining deep learning with hyperspectral medical images and achieve some progress. This paper introduces the principles and techniques of hyperspectral imaging systems, summarizes the common medical hyperspectral imaging systems, and summarizes the progress of some emerging spectral imaging systems through analyzing the literature. In particular, this article introduces the more frequently used medical hyperspectral images and the pre-processing techniques of the spectra, and in other sections, it discusses the main developments of medical hyperspectral combined with deep learning for disease diagnosis. On the basis of the previous review, tne limited factors in the study on the application of deep learning to hyperspectral medical images are outlined, promising research directions are summarized, and the future research prospects are provided for subsequent scholars.
Collapse
Affiliation(s)
- Rong Cui
- College of Electronic and Information Engineering, Changchun University, Changchun 130022, China
| | - He Yu
- College of Electronic and Information Engineering, Changchun University, Changchun 130022, China
- Jilin Provincial Key Laboratory of Human Health Status Identification and Function Enhancement, Changchun University, Changchun 130022, China
| | - Tingfa Xu
- Image Engineering & Video Technology Lab, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
- Beijing Institute of Technology Chongqing Innovation Center, Chongqing 401120, China
| | - Xiaoxue Xing
- College of Electronic and Information Engineering, Changchun University, Changchun 130022, China
- Jilin Provincial Key Laboratory of Human Health Status Identification and Function Enhancement, Changchun University, Changchun 130022, China
| | - Xiaorui Cao
- College of Electronic and Information Engineering, Changchun University, Changchun 130022, China
| | - Kang Yan
- College of Electronic and Information Engineering, Changchun University, Changchun 130022, China
| | - Jiexi Chen
- College of Electronic and Information Engineering, Changchun University, Changchun 130022, China
| |
Collapse
|