Leping Xiao;Jianyu Wang;Yi Wang;Ziyu Zhan;Zuoqiang Shi;Lingyun Qiu;Xing Fu
{"title":"Fast Non-Line-of-Sight Imaging With Hybrid Super-Resolution Network Over 18 m","authors":"Leping Xiao;Jianyu Wang;Yi Wang;Ziyu Zhan;Zuoqiang Shi;Lingyun Qiu;Xing Fu","doi":"10.1109/TCI.2024.3463964","DOIUrl":null,"url":null,"abstract":"Non-line-of-sight (NLOS) imaging technique aims at visualizing hidden objects from light of multiple reflections. For most existing methods, densely raster-scanned transients with long exposure time are routinely used, while approaches employing fewer points are confronted with a trade-off between the computation time and the image quality, both of which hinder the practical implementation of fast NLOS imaging. In this paper, we propose a hybrid super-resolution pipeline for image reconstruction and quality enhancement with only 8×8 scanning points. Besides, we implement a non-coaxial transceiver configuration and illustrate the first auto-calibration method for out-of-lab NLOS configuration, which costs only 40 s and performs well at a distance of 18.69 m. Results on both experimental data and public dataset indicate that the proposed method exhibits strong generalization capabilities, yielding faithful reconstructions with the resolution of 256×256 under different noise models. Furthermore, we demonstrate the importance of matching the noise model with the experimental dataset. We believe our approach shows great promise to NLOS imaging acceleration with lower acquisition, calibration and computation time.","PeriodicalId":56022,"journal":{"name":"IEEE Transactions on Computational Imaging","volume":"10 ","pages":"1439-1448"},"PeriodicalIF":4.2000,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Computational Imaging","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10684139/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Non-line-of-sight (NLOS) imaging technique aims at visualizing hidden objects from light of multiple reflections. For most existing methods, densely raster-scanned transients with long exposure time are routinely used, while approaches employing fewer points are confronted with a trade-off between the computation time and the image quality, both of which hinder the practical implementation of fast NLOS imaging. In this paper, we propose a hybrid super-resolution pipeline for image reconstruction and quality enhancement with only 8×8 scanning points. Besides, we implement a non-coaxial transceiver configuration and illustrate the first auto-calibration method for out-of-lab NLOS configuration, which costs only 40 s and performs well at a distance of 18.69 m. Results on both experimental data and public dataset indicate that the proposed method exhibits strong generalization capabilities, yielding faithful reconstructions with the resolution of 256×256 under different noise models. Furthermore, we demonstrate the importance of matching the noise model with the experimental dataset. We believe our approach shows great promise to NLOS imaging acceleration with lower acquisition, calibration and computation time.
期刊介绍:
The IEEE Transactions on Computational Imaging will publish articles where computation plays an integral role in the image formation process. Papers will cover all areas of computational imaging ranging from fundamental theoretical methods to the latest innovative computational imaging system designs. Topics of interest will include advanced algorithms and mathematical techniques, model-based data inversion, methods for image and signal recovery from sparse and incomplete data, techniques for non-traditional sensing of image data, methods for dynamic information acquisition and extraction from imaging sensors, software and hardware for efficient computation in imaging systems, and highly novel imaging system design.