Ensemble Learning Priors Driven Deep Unfolding for Scalable Video Snapshot Compressive Imaging

Chengshuai Yang, Shiyu Zhang, Xin Yuan

Abstract: Snapshot compressive imaging (SCI) can record a 3D datacube by a 2D measurement and algorithmically reconstruct the desired 3D information from that 2D measurement. The reconstruction algorithm thus plays a vital role in SCI. Recently, deep learning (DL) has demonstrated outstanding performance in reconstruction, leading to better results than conventional optimization-based methods. Therefore, it is desirable to improve DL reconstruction performance for SCI. Existing DL algorithms are limited by two bottlenecks: 1) a high-accuracy network is usually large and requires a long running time; 2) DL algorithms are limited by scalability, i.e., a well-trained network cannot generally be applied to new systems. To this end, this paper proposes to use ensemble learning priors in DL to achieve high reconstruction speed and accuracy in a single network. Furthermore, we develop the scalable learning approach during training to empower DL to handle data of different sizes without additional training. Extensive results on both simulation and real datasets demonstrate the superiority of our proposed algorithm. The code and model can be accessed at https://github.com/integritynoble/ELP-Unfolding/tree/master.