Yang Peihao, Kong Linghe, Liu Xiao-Yang, Yuan Xin, Chen Guihai
IEEE Trans Image Process. 2020 May 6. doi: 10.1109/TIP.2020.2989550.
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.
快照压缩成像（SCI）是一种利用低维传感器捕获高维数据的很有前景的方法。通过对现成相机进行适度修改，SCI相机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），它利用了图像在频域和剪切波域表示中的稀疏性。为此，我们首先在乘子交替方向法（ADMM）框架下推导我们的SeSCI算法。然后我们提出了SeSCI算法的一种有效解决方案。此外，我们证明了改进后的SeSCI算法收敛到一个不动点。在SCI相机捕获的合成数据和真实数据上的实验结果证明了SeSCI的显著优势，其在峰值信噪比（PSNR）方面比传统算法高出2dB以上。同时，SeSCI比现有最先进算法实现了100倍以上的加速。
