基于深度学习的非视域成像

Translated title of the contribution: Non-Line-of-Sight Imaging Through Deep Learning

Tingyi Yu, Mu Qiao, Honglin Liu, Shensheng Han

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Aiming at the problem of non-line-of-sight imaging under incoherent illumination, we propose a solution based on deep learning. Combining the classical semantic segmentation and residual model in the field of computer vision, a URNet network structure is constructed and the classical bottleneck layer structure is improved. The experimental results show that the improved model has more details of recovery images and generalization ability. Compared with speckle autocorrelation imaging method under incoherent illumination, the recovery performance of this method is greatly improved.

Translated title of the contributionNon-Line-of-Sight Imaging Through Deep Learning
Original languageChinese (Traditional)
Article number0711002
JournalGuangxue Xuebao/Acta Optica Sinica
Volume39
Issue number7
DOIs
StatePublished - Jul 10 2019
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics

Keywords

  • Deep learning
  • Imaging systems
  • Non-line-of-sight imaging
  • Residual model
  • Semantic segmentation

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