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.
|Number of pages||16|
|Publication status||Accepted/In press - 1 Sep 2020|
- ultrafast imaging
- computational photography
- deep learning
- compressed ultrafast photography
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.