Distortion Correction of Two-Dimensional Spectral Image Based on Neural Network
Two-dimensional spectral images are generally distorted. Spectrum extraction operation is affected by such distortion, which reduces the quality of one-dimensional spectral data. Aiming at above problem, an effective correction method for the distorted two-dimensional spectral images based on neural...
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| Format: | Article |
| Language: | zho |
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Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2023-07-01
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| Series: | Jisuanji kexue yu tansuo |
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| Online Access: | http://fcst.ceaj.org/fileup/1673-9418/PDF/2201072.pdf |
| _version_ | 1797786650276790272 |
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| author | YIN Qian, WANG Yan, GUO Ping, ZHENG Xin |
| author_facet | YIN Qian, WANG Yan, GUO Ping, ZHENG Xin |
| author_sort | YIN Qian, WANG Yan, GUO Ping, ZHENG Xin |
| collection | DOAJ |
| description | Two-dimensional spectral images are generally distorted. Spectrum extraction operation is affected by such distortion, which reduces the quality of one-dimensional spectral data. Aiming at above problem, an effective correction method for the distorted two-dimensional spectral images based on neural network is proposed. Firstly, by extracting the center line of each fiber from the flat-field spectrum and fitting the equal-wavelength line at each specific wavelength from the calibration lamp spectrum, data that represent distortion characteristics from two-dimensional spectral images can be obtained. The training samples are thus constructed according to these two sets of feature lines. Secondly, a neural network model is then designed and trained to fit the relation between the pixel coordinates of the image before and after correction. Therefore, all pixel coordinate values of the corrected image can be calculated by the model. Finally, the flux values of the corrected image are filled one-to-one in accord with the flux value of the original distorted image. The correction experiments are carried out with the flat-field spectrum, calibration lamp spectrum, and object spectrum respectively. The spectral extraction results of the object spectrum before and after correction are compared. Experimental results prove that the method can correct the distorted two-dimensional spectral image effectively and improve quality of one-dimensional spectral data to an extent. |
| first_indexed | 2024-03-13T01:10:51Z |
| format | Article |
| id | doaj.art-93e7c90efd1b4e3281bf5314f3bb3d04 |
| institution | Directory Open Access Journal |
| issn | 1673-9418 |
| language | zho |
| last_indexed | 2024-03-13T01:10:51Z |
| publishDate | 2023-07-01 |
| publisher | Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press |
| record_format | Article |
| series | Jisuanji kexue yu tansuo |
| spelling | doaj.art-93e7c90efd1b4e3281bf5314f3bb3d042023-07-06T01:16:31ZzhoJournal of Computer Engineering and Applications Beijing Co., Ltd., Science PressJisuanji kexue yu tansuo1673-94182023-07-011771622163310.3778/j.issn.1673-9418.2201072Distortion Correction of Two-Dimensional Spectral Image Based on Neural NetworkYIN Qian, WANG Yan, GUO Ping, ZHENG Xin01. School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China 2. School of Systems Science, Beijing Normal University, Beijing 100875, ChinaTwo-dimensional spectral images are generally distorted. Spectrum extraction operation is affected by such distortion, which reduces the quality of one-dimensional spectral data. Aiming at above problem, an effective correction method for the distorted two-dimensional spectral images based on neural network is proposed. Firstly, by extracting the center line of each fiber from the flat-field spectrum and fitting the equal-wavelength line at each specific wavelength from the calibration lamp spectrum, data that represent distortion characteristics from two-dimensional spectral images can be obtained. The training samples are thus constructed according to these two sets of feature lines. Secondly, a neural network model is then designed and trained to fit the relation between the pixel coordinates of the image before and after correction. Therefore, all pixel coordinate values of the corrected image can be calculated by the model. Finally, the flux values of the corrected image are filled one-to-one in accord with the flux value of the original distorted image. The correction experiments are carried out with the flat-field spectrum, calibration lamp spectrum, and object spectrum respectively. The spectral extraction results of the object spectrum before and after correction are compared. Experimental results prove that the method can correct the distorted two-dimensional spectral image effectively and improve quality of one-dimensional spectral data to an extent.http://fcst.ceaj.org/fileup/1673-9418/PDF/2201072.pdftwo-dimensional spectra; neural network; image processing; image correction |
| spellingShingle | YIN Qian, WANG Yan, GUO Ping, ZHENG Xin Distortion Correction of Two-Dimensional Spectral Image Based on Neural Network Jisuanji kexue yu tansuo two-dimensional spectra; neural network; image processing; image correction |
| title | Distortion Correction of Two-Dimensional Spectral Image Based on Neural Network |
| title_full | Distortion Correction of Two-Dimensional Spectral Image Based on Neural Network |
| title_fullStr | Distortion Correction of Two-Dimensional Spectral Image Based on Neural Network |
| title_full_unstemmed | Distortion Correction of Two-Dimensional Spectral Image Based on Neural Network |
| title_short | Distortion Correction of Two-Dimensional Spectral Image Based on Neural Network |
| title_sort | distortion correction of two dimensional spectral image based on neural network |
| topic | two-dimensional spectra; neural network; image processing; image correction |
| url | http://fcst.ceaj.org/fileup/1673-9418/PDF/2201072.pdf |
| work_keys_str_mv | AT yinqianwangyanguopingzhengxin distortioncorrectionoftwodimensionalspectralimagebasedonneuralnetwork |