RISIR: Rapid Infrared Spectral Imaging Restoration Model for Industrial Material Detection in Intelligent Video Systems
Material detection by industrial infrared imaging spectrometer (IRIS) is a key technique in multiple industrial applications, including garbage collection, material analysis, and robot vision. However, IRIS often suffers from overlapped bands and random noises, which limit the precision of subsequen...
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| Vydané v: | IEEE transactions on industrial informatics s. 1 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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IEEE
2024
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| ISSN: | 1551-3203, 1941-0050 |
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| Abstract | Material detection by industrial infrared imaging spectrometer (IRIS) is a key technique in multiple industrial applications, including garbage collection, material analysis, and robot vision. However, IRIS often suffers from overlapped bands and random noises, which limit the precision of subsequent processing. In this article, we propose a novel Gabor transform-based mid-wave infrared (MWIR) spectrum restoration model by successfully exploring the intrinsic structure of the clean MWIR spectrum from the degraded one. First, a total variation-regularized Gabor coefficient adjustment descriptor is designed and incorporated into the spectrum restoration model. In addition to adjusting the Gabor coefficient distribution by total variation-norm, the L2-norm of gradient is leveraged to regulate the smoothness of the instrument degradation function. Then, the proposed model is inferred using an efficient optimization approach based on split Bregman iteration method and alternating minimization algorithm. Quantitative and qualitative experimental results demonstrate that the proposed model favorably outperforms the state-of-the-art approaches. The restored high-resolution MWIR spectrum can be used to rapidly detect different materials in intelligent video systems. |
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| AbstractList | Material detection by industrial infrared imaging spectrometer (IRIS) is a key technique in multiple industrial applications, including garbage collection, material analysis, and robot vision. However, IRIS often suffers from overlapped bands and random noises, which limit the precision of subsequent processing. In this article, we propose a novel Gabor transform-based mid-wave infrared (MWIR) spectrum restoration model by successfully exploring the intrinsic structure of the clean MWIR spectrum from the degraded one. First, a total variation-regularized Gabor coefficient adjustment descriptor is designed and incorporated into the spectrum restoration model. In addition to adjusting the Gabor coefficient distribution by total variation-norm, the L2-norm of gradient is leveraged to regulate the smoothness of the instrument degradation function. Then, the proposed model is inferred using an efficient optimization approach based on split Bregman iteration method and alternating minimization algorithm. Quantitative and qualitative experimental results demonstrate that the proposed model favorably outperforms the state-of-the-art approaches. The restored high-resolution MWIR spectrum can be used to rapidly detect different materials in intelligent video systems. |
| Author | Zhang, Zhaoli Liu, Sannyuya Liu, Tingting Liu, Hai Li, Y. F. |
| Author_xml | – sequence: 1 givenname: Tingting surname: Liu fullname: Liu, Tingting email: tingtingliu89619@gmail.com organization: department of Mechanical Engineering, City University of Hong Kong, 53025 Hong Kong, Hong Kong Hong Kong - (e-mail: tingtingliu89619@gmail.com) – sequence: 2 givenname: Y. F. surname: Li fullname: Li, Y. F. email: meyfli@cityu.edu.hk organization: City University, Kowloon Hong Kong x11 x22 (e-mail: meyfli@cityu.edu.hk) – sequence: 3 givenname: Hai surname: Liu fullname: Liu, Hai email: hliu97@cityu.edu.hk organization: department of Mechanical Engineering, City University of Hong Kong, Hong Kong Hong Kong (e-mail: hliu97@cityu.edu.hk) – sequence: 4 givenname: Zhaoli surname: Zhang fullname: Zhang, Zhaoli email: zl.zhang@mail.ccnu.edu.cn organization: Wuhan, Hubei China 430079 (e-mail: zl.zhang@mail.ccnu.edu.cn) – sequence: 5 givenname: Sannyuya surname: Liu fullname: Liu, Sannyuya email: lsy5918@mail.ccnu.edu.cn organization: Wuhan, Hubei China 430079 (e-mail: lsy5918@mail.ccnu.edu.cn) |
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| SubjectTerms | Degradation Image resolution Imaging industrial material detection Informatics infrared spectrometer Intelligent video system Iris optical data processing Optical filters recycling method spectral imaging Transforms |
| Title | RISIR: Rapid Infrared Spectral Imaging Restoration Model for Industrial Material Detection in Intelligent Video Systems |
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