Optimized Method Based on Subspace Merging for Spectral Reflectance Recovery
The similarity between samples is an important factor for spectral reflectance recovery. The current way of selecting samples after dividing dataset does not take subspace merging into account. An optimized method based on subspace merging for spectral recovery is proposed from single RGB trichromat...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/6/3056 |
| _version_ | 1797609037091569664 |
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| author | Yifan Xiong Guangyuan Wu Xiaozhou Li |
| author_facet | Yifan Xiong Guangyuan Wu Xiaozhou Li |
| author_sort | Yifan Xiong |
| collection | DOAJ |
| description | The similarity between samples is an important factor for spectral reflectance recovery. The current way of selecting samples after dividing dataset does not take subspace merging into account. An optimized method based on subspace merging for spectral recovery is proposed from single RGB trichromatic values in this paper. Each training sample is equivalent to a separate subspace, and the subspaces are merged according to the Euclidean distance. The merged center point for each subspace is obtained through many iterations, and subspace tracking is used to determine the subspace where each testing sample is located for spectral recovery. After obtaining the center points, these center points are not the actual points in the training samples. The nearest distance principle is used to replace the center points with the point in the training samples, which is the process of representative sample selection. Finally, these representative samples are used for spectral recovery. The effectiveness of the proposed method is tested by comparing it with the existing methods under different illuminants and cameras. Through the experiments, the results show that the proposed method not only shows good results in terms of spectral and colorimetric accuracy, but also in the selection representative samples. |
| first_indexed | 2024-03-11T05:55:59Z |
| format | Article |
| id | doaj.art-1bd68624360c4e0d96d71b481eed7893 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T05:55:59Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-1bd68624360c4e0d96d71b481eed78932023-11-17T13:45:20ZengMDPI AGSensors1424-82202023-03-01236305610.3390/s23063056Optimized Method Based on Subspace Merging for Spectral Reflectance RecoveryYifan Xiong0Guangyuan Wu1Xiaozhou Li2Faculty of Light Industry, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, ChinaFaculty of Light Industry, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, ChinaState Key Laboratory of Biobased Material and Green Papermaking, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, ChinaThe similarity between samples is an important factor for spectral reflectance recovery. The current way of selecting samples after dividing dataset does not take subspace merging into account. An optimized method based on subspace merging for spectral recovery is proposed from single RGB trichromatic values in this paper. Each training sample is equivalent to a separate subspace, and the subspaces are merged according to the Euclidean distance. The merged center point for each subspace is obtained through many iterations, and subspace tracking is used to determine the subspace where each testing sample is located for spectral recovery. After obtaining the center points, these center points are not the actual points in the training samples. The nearest distance principle is used to replace the center points with the point in the training samples, which is the process of representative sample selection. Finally, these representative samples are used for spectral recovery. The effectiveness of the proposed method is tested by comparing it with the existing methods under different illuminants and cameras. Through the experiments, the results show that the proposed method not only shows good results in terms of spectral and colorimetric accuracy, but also in the selection representative samples.https://www.mdpi.com/1424-8220/23/6/3056spectral recoverycamera responsesrepresentative samplessubspace merging |
| spellingShingle | Yifan Xiong Guangyuan Wu Xiaozhou Li Optimized Method Based on Subspace Merging for Spectral Reflectance Recovery Sensors spectral recovery camera responses representative samples subspace merging |
| title | Optimized Method Based on Subspace Merging for Spectral Reflectance Recovery |
| title_full | Optimized Method Based on Subspace Merging for Spectral Reflectance Recovery |
| title_fullStr | Optimized Method Based on Subspace Merging for Spectral Reflectance Recovery |
| title_full_unstemmed | Optimized Method Based on Subspace Merging for Spectral Reflectance Recovery |
| title_short | Optimized Method Based on Subspace Merging for Spectral Reflectance Recovery |
| title_sort | optimized method based on subspace merging for spectral reflectance recovery |
| topic | spectral recovery camera responses representative samples subspace merging |
| url | https://www.mdpi.com/1424-8220/23/6/3056 |
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