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\times $ over the state-of-the-art algorithm.

中文翻译：

---

Shearlet 增强快照压缩成像。


快照压缩成像（SCI）是一种利用低维传感器捕获高维数据的有前途的方法。通过对现成相机进行适度修改，SCI 相机可将多个帧编码为单个测量帧。然后可以通过重建算法检索这些相关帧。现有的重建算法存在速度慢或保真度低的问题。在本文中，我们提出了一种新颖的重建算法，即剪切波增强快照压缩成像（SeSCI），该算法利用了频域和剪切波域中图像表示的稀疏性。为此，我们首先在乘法器交替方向法（ADMM）框架下推导我们的 SeSCI 算法。然后我们提出了 SeSCI 算法的有效解决方案。此外，我们证明了改进的SeSCI算法收敛到一个不动点。 SCI相机捕获的合成数据和真实数据的实验结果证明了SeSCI的显着优势，其PSNR优于传统算法2dB以上。同时，SeSCI 实现了超过$100\次$超过最先进的算法。