Unsupervised reconstruction with low-rank tensor embedding based on spatial-intensity-temporal constraints for compressed ultrafast photography

Compressed ultrafast photography (CUP) is a computational imaging technique that can simultaneously achieve an imaging speed of 10 13 frames per second and a sequence depth of hundreds of frames. It is a powerful tool for observing unrepeatable ultrafast physical processes. However, since the forward model of CUP is a data compression process, the reconstruction process is an ill-posed problem. This causes inconvenience in the practical application of CUP, especially in those scenes with complex temporal behavior, high noise level and compression ratio. In this paper, the CUP system model based on spatial-intensity-temporal constraints is proposed by adding an additional charge-coupled device (CCD) camera to constrain the spatial and intensity behaviors of the dynamic scene and an additional narrow-slit streak camera to constrain the temporal behavior of the dynamic scene. Additionally, the unsupervised deep learning CUP reconstruction algorithm with low-rank tensor embedding is also proposed. The algorithm enhances the low-rankness of the reconstructed image by maintaining the low-rank structure of the dynamic scene and effectively utilizes the implicit prior information of the neural network and the hardware physical model. The proposed joint learning model enables high-quality reconstruction of complex dynamic scenes without training datasets. The simulation and experimental results demonstrate the application prospect of the proposed joint learning model in complex ultrafast physical phenomena imaging.