Axin Fan, Tingfa Xu, Geer Teng, Xi Wang, Chang Xu, Yuhan Zhang, Jianan Li
{"title":"Multi-angle and full-Stokes polarization multispectral images using quarter-wave plate and tunable filter.","authors":"Axin Fan, Tingfa Xu, Geer Teng, Xi Wang, Chang Xu, Yuhan Zhang, Jianan Li","doi":"10.1038/s41597-024-04233-9","DOIUrl":null,"url":null,"abstract":"<p><p>Polarization multispectral imaging has advanced significantly due to its robust information representation capability. Imaging application requires rigorous simulation evaluation and experimental validation using standardized datasets. However, the current full-Stokes polarization multispectral images (FSPMI) dataset, while providing simulation data, is limited by image drift and spectral bands. To overcome these limitations and supplement experimental data, this paper introduces the multi-angle and full-Stokes polarization multispectral images (MAFS-PMI) dataset. The imaging system utilizes a rotatable quarter-wave plate (QWP) and a fixed liquid crystal tunable filter (LCTF) to modulate polarization information. Meanwhile, the LCTF allows switching between multiple spectral bands. The acquired multi-angle polarization multispectral images facilitate the experimental validation of encoding strategies and reconstruction algorithms. Additionally, the derived full-Stokes polarization multispectral images enable the simulation evaluation of imaging methods. The MAFS-PMI dataset involves 73 fast axis angles (0° to 180°), four Stokes parameters, five polarization parameters, 35 spectral bands (520 nm to 690 nm), 400 × 400 pixels, and 12 distinct objects. This dataset offers a valuable resource for developing advanced imaging methods.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"11 1","pages":"1401"},"PeriodicalIF":5.8000,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-024-04233-9","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Polarization multispectral imaging has advanced significantly due to its robust information representation capability. Imaging application requires rigorous simulation evaluation and experimental validation using standardized datasets. However, the current full-Stokes polarization multispectral images (FSPMI) dataset, while providing simulation data, is limited by image drift and spectral bands. To overcome these limitations and supplement experimental data, this paper introduces the multi-angle and full-Stokes polarization multispectral images (MAFS-PMI) dataset. The imaging system utilizes a rotatable quarter-wave plate (QWP) and a fixed liquid crystal tunable filter (LCTF) to modulate polarization information. Meanwhile, the LCTF allows switching between multiple spectral bands. The acquired multi-angle polarization multispectral images facilitate the experimental validation of encoding strategies and reconstruction algorithms. Additionally, the derived full-Stokes polarization multispectral images enable the simulation evaluation of imaging methods. The MAFS-PMI dataset involves 73 fast axis angles (0° to 180°), four Stokes parameters, five polarization parameters, 35 spectral bands (520 nm to 690 nm), 400 × 400 pixels, and 12 distinct objects. This dataset offers a valuable resource for developing advanced imaging methods.
期刊介绍:
Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data.
The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.