Single-shot compressed ultrafast photography based on U-net network
Anke Zhang, Jiamin Wu, Jinli Suo, Lu Fang, Hui Qiao, David Li, Shian Zhang, Jintao Fan, Dalong Qi, Qionghai Dai, Chengquan Pei
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.