{"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}
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.