Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105325
DC Field | Value | Language |
---|---|---|
dc.contributor | School of Fashion and Textiles | - |
dc.creator | Yao, P | - |
dc.creator | Wu, H | - |
dc.creator | Xin, JH | - |
dc.date.accessioned | 2024-04-12T06:51:41Z | - |
dc.date.available | 2024-04-12T06:51:41Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/105325 | - |
dc.language.iso | en | en_US |
dc.publisher | Molecular Diversity Preservation International (MDPI) | en_US |
dc.rights | © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights | The following publication Yao P, Wu H, Xin JH. Improving Generalizability of Spectral Reflectance Reconstruction Using L1-Norm Penalization. Sensors. 2023; 23(2):689 is available at https://doi.org/10.3390/s23020689. | en_US |
dc.subject | Color reproduction | en_US |
dc.subject | Multispectral imaging | en_US |
dc.subject | Spectral reconstruction | en_US |
dc.subject | Spectral reflectance | en_US |
dc.title | Improving generalizability of spectral reflectance reconstruction using L1-norm penalization | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 23 | - |
dc.identifier.issue | 2 | - |
dc.identifier.doi | 10.3390/s23020689 | - |
dcterms.abstract | Spectral reflectance reconstruction for multispectral images (such as Weiner estimation) may perform sub-optimally when the object being measured has a texture that is not in the training set. The accuracy of the reconstruction is significantly lower without training samples. We propose an improved reflectance reconstruction method based on L1-norm penalization to solve this issue. Using L1-norm, our method can provide the transformation matrix with the favorable sparse property, which can help to achieve better results when measuring the unseen samples. We verify the proposed method by reconstructing spectral reflection for four types of materials (cotton, paper, polyester, and nylon) captured by a multispectral imaging system. Each of the materials has its texture and there are 204 samples in each of the materials/textures in the experiments. The experimental results show that when the texture is not included in the training dataset, L1-norm can achieve better results compared with existing methods using colorimetric measure (i.e., color difference) and shows consistent accuracy across four kinds of materials. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Sensors, Jan. 2023, v. 23, no. 2, 689 | - |
dcterms.isPartOf | Sensors | - |
dcterms.issued | 2023-01 | - |
dc.identifier.scopus | 2-s2.0-85146729640 | - |
dc.identifier.pmid | 36679486 | - |
dc.identifier.eissn | 1424-8220 | - |
dc.identifier.artn | 689 | - |
dc.description.validate | 202403 bcvc | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Natural Science Foundation of China | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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sensors-23-00689-v2.pdf | 1.7 MB | Adobe PDF | View/Open |
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