Shearlet Enhanced Snapshot Compressive Imaging

Abstract

Snapshot compressive imaging (SCI) is a promising approach to capture high-dimensional data with low dimensional sensors. With modest modifications to off-the-shelf cameras, SCI cameras encode multiple frames into a single measurement frame. These correlated frames can then be retrieved by reconstruction algorithms. Existing reconstruction algorithms suffer from low speed or low fidelity. In this paper, we propose a novel reconstruction algorithm, namely, Shearlet enhanced Snapshot Compressive Imaging (SeSCI), which exploits the sparsity of the image representation in both frequency domain and shearlet domain. Towards this end, we first derive our SeSCI algorithm under the alternating direction method of multipliers (ADMM) framework. We then propose an efficient solution of SeSCI algorithm. Moreover, we prove that the improved SeSCI algorithm converges to a fixed point. Experimental results on both synthetic data and real data captured by SCI cameras demonstrate the significant advantages of SeSCI, which outperforms the conventional algorithms by more than 2dB in PSNR. At the same time, the SeSCI achieves a speed-up more than 100× over the state-of-the-art algorithm.