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

Abstract

Snapshot compressive imaging (SCI) can record the 3D datacube by a 2D measurement and from this 2D measurement to reconstruct the desired 3D information by algorithms. 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 desired 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 in general can not be applied to new systems. Towards this end, this paper proposes to use ensemble learning priors in DL to keep 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 will be released.