{"title":"Snapshot Hyperspectral Light Field Imaging","authors":"Zhiwei Xiong, Lizhi Wang, Huiqun Li, Dong Liu, Feng Wu","doi":"10.1109/CVPR.2017.727","DOIUrl":null,"url":null,"abstract":"This paper presents the first snapshot hyperspectral light field imager in practice. Specifically, we design a novel hybrid camera system to obtain two complementary measurements that sample the angular and spectral dimensions respectively. To recover the full 5D hyperspectral light field from the severely undersampled measurements, we then propose an efficient computational reconstruction algorithm by exploiting the large correlations across the angular and spectral dimensions through self-learned dictionaries. Simulation on an elaborate hyperspectral light field dataset validates the effectiveness of the proposed approach. Hardware experimental results demonstrate that, for the first time to our knowledge, a 5D hyperspectral light field containing 9x9 angular views and 27 spectral bands can be acquired in a single shot.","PeriodicalId":6631,"journal":{"name":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"1 1","pages":"6873-6881"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"36","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CVPR.2017.727","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 36
Abstract
This paper presents the first snapshot hyperspectral light field imager in practice. Specifically, we design a novel hybrid camera system to obtain two complementary measurements that sample the angular and spectral dimensions respectively. To recover the full 5D hyperspectral light field from the severely undersampled measurements, we then propose an efficient computational reconstruction algorithm by exploiting the large correlations across the angular and spectral dimensions through self-learned dictionaries. Simulation on an elaborate hyperspectral light field dataset validates the effectiveness of the proposed approach. Hardware experimental results demonstrate that, for the first time to our knowledge, a 5D hyperspectral light field containing 9x9 angular views and 27 spectral bands can be acquired in a single shot.