基于全卷积神经网络的单像素重建的空间光谱增强

Kevin Lozano, L. Galvis, H. Arguello
{"title":"基于全卷积神经网络的单像素重建的空间光谱增强","authors":"Kevin Lozano, L. Galvis, H. Arguello","doi":"10.1109/ColCACI50549.2020.9247903","DOIUrl":null,"url":null,"abstract":"Spectral images (SI) contain a large amount of information, which increases the cost of their collection, storage, and processing. Compressive spectral imaging (CSI) methods allow the reconstruction of that information from a small set of random projections. However, compressed sensing with high spatial-spectral resolution requires expensive sensors. The single-pixel camera is an architecture that provides a low-cost solution for compressive SI acquisition, but with a significant limitation in image resolution. This work proposes a deep learning approach by means of a fully convolutional neural network (FCNN) with the ability to retrieve the spatio-spectral information from a single-pixel reconstruction in order to produce a spatio-spectral enhancement, suitable for applications requiring higher quality and low-cost acquisition systems. Simulations and experimental results show that the trained FCNN with a limited data set improves the quality of the single-pixel reconstruction in up to 20 dB, without requiring additional measurements. The performance of the proposed approach is compared against the traditional method of the single-pixel reconstruction.","PeriodicalId":446750,"journal":{"name":"2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Spatio-Spectral Enhancement of Single-Pixel Reconstruction by a Fully Convolutional Neural Network\",\"authors\":\"Kevin Lozano, L. Galvis, H. Arguello\",\"doi\":\"10.1109/ColCACI50549.2020.9247903\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Spectral images (SI) contain a large amount of information, which increases the cost of their collection, storage, and processing. Compressive spectral imaging (CSI) methods allow the reconstruction of that information from a small set of random projections. However, compressed sensing with high spatial-spectral resolution requires expensive sensors. The single-pixel camera is an architecture that provides a low-cost solution for compressive SI acquisition, but with a significant limitation in image resolution. This work proposes a deep learning approach by means of a fully convolutional neural network (FCNN) with the ability to retrieve the spatio-spectral information from a single-pixel reconstruction in order to produce a spatio-spectral enhancement, suitable for applications requiring higher quality and low-cost acquisition systems. Simulations and experimental results show that the trained FCNN with a limited data set improves the quality of the single-pixel reconstruction in up to 20 dB, without requiring additional measurements. The performance of the proposed approach is compared against the traditional method of the single-pixel reconstruction.\",\"PeriodicalId\":446750,\"journal\":{\"name\":\"2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020)\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-08-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ColCACI50549.2020.9247903\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ColCACI50549.2020.9247903","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

光谱图像包含大量的信息,这增加了其采集、存储和处理的成本。压缩光谱成像(CSI)方法允许从一小组随机投影中重建该信息。然而,高空间光谱分辨率的压缩感知需要昂贵的传感器。单像素相机是一种为压缩SI采集提供低成本解决方案的架构,但在图像分辨率方面存在很大限制。这项工作提出了一种深度学习方法,通过全卷积神经网络(FCNN),能够从单像素重建中检索空间光谱信息,以产生空间光谱增强,适用于需要更高质量和低成本采集系统的应用。仿真和实验结果表明,在不需要额外测量的情况下,使用有限数据集训练的FCNN可以提高单像素重建的质量,最高可达20 dB。将该方法与传统的单像素重建方法进行了性能比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spatio-Spectral Enhancement of Single-Pixel Reconstruction by a Fully Convolutional Neural Network
Spectral images (SI) contain a large amount of information, which increases the cost of their collection, storage, and processing. Compressive spectral imaging (CSI) methods allow the reconstruction of that information from a small set of random projections. However, compressed sensing with high spatial-spectral resolution requires expensive sensors. The single-pixel camera is an architecture that provides a low-cost solution for compressive SI acquisition, but with a significant limitation in image resolution. This work proposes a deep learning approach by means of a fully convolutional neural network (FCNN) with the ability to retrieve the spatio-spectral information from a single-pixel reconstruction in order to produce a spatio-spectral enhancement, suitable for applications requiring higher quality and low-cost acquisition systems. Simulations and experimental results show that the trained FCNN with a limited data set improves the quality of the single-pixel reconstruction in up to 20 dB, without requiring additional measurements. The performance of the proposed approach is compared against the traditional method of the single-pixel reconstruction.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信