Single-shot compressed ultrafast photography based on U-net network

Anke Zhang, Jiamin Wu, Jinli Suo, Lu Fang, Hui Qiao, David Day-Uei Li, Shian Zhang, Jiantao Fan, Dalong Qi, Qionghai Dai, Chengquan Pei

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Abstract

The compressive ultrafast photography (CUP) has achieved real-time femtosecond imaging based on the compressive-sensing methods. However, the reconstruction performance usually suffers from artifacts brought by strong noise, aberration, and distortion, which prevents its applications. We propose a deep compressive ultrafast photography (DeepCUP) method. Various numerical simulations have been demonstrated on both the MNIST and UCF-101 datasets and compared with other state-of-the-art algorithms. The result shows that our DeepCUP has a superior performance in both PSNR and SSIM compared to previous compressed-sensing methods. We also illustrate the outstanding performance of the proposed method under system errors and noise in comparison to other methods.

Original languageEnglish
Article number398083
Pages (from-to)39299-39310
Number of pages12
JournalOptics Express
Volume28
Issue number26
Early online date14 Dec 2020
DOIs
Publication statusPublished - 21 Dec 2020

Keywords

  • ultrafast imaging
  • computational photography
  • deep learning
  • compressed ultrafast photography

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