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, Chengquan Pei, Qionghai Dai

Research output: Contribution to journalArticle


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 to previous compressed-sensing methods. We also illustrate the outstanding performance of the proposed method under system errors and noise comparing to other methods.
Original languageEnglish
Number of pages16
JournalOptics Express
Publication statusAccepted/In press - 1 Sep 2020


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

Cite this

Zhang, A., Wu, J., Suo, J., Fang, L., Qiao, H., Li, D. D-U., Zhang, S., Fan, J., Qi, D., Pei, C., & Dai, Q. (Accepted/In press). Single-shot compressed ultrafast photography based on U-net network. Optics Express.