Deep-learning-based image reconstruction for compressed ultrafast photography.
Deep-learning-based image reconstruction for compressed ultrafast photography.
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DOI：
10.1364/ol.397717
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发表时间：
2020-08-15
期刊：
影响因子：
3.6
通讯作者：
Gao L
中科院分区：
文献类型：
--
作者：
Ma Y;Feng X;Gao L
压缩超快摄影（CUP）是一种计算光学成像技术，可以以前所未有的速度捕捉瞬态动力学。目前，CUP的图像重建依赖于迭代算法，这是耗时的，往往产生非最佳的图像质量。为了解决这个问题，我们开发了一种基于深度学习的CUP重建方法，大大提高了图像质量和重建速度。大型三维（3D）事件数据立方体（x，y，t）（x，y，空间坐标; t，时间）的有效深度学习重建的一个关键创新是，我们将原始数据立方体分解为大规模并行的二维（2D）成像子问题，这些子问题通过深度神经网络解决要简单得多。我们验证了我们的方法模拟和实验数据。
Compressed ultrafast photography (CUP) is a computational optical imaging technique that can capture transient dynamics at an unprecedented speed. Currently, the image reconstruction of CUP relies on iterative algorithms, which are time-consuming and often yield nonoptimal image quality. To solve this problem, we develop a deep-learning-based method for CUP reconstruction that substantially improves the image quality and reconstruction speed. A key innovation toward efficient deep learning reconstruction of a large three-dimensional (3D) event datacube (x, y, t) (x, y, spatial coordinate; t, time) is that we decompose the original datacube into massively parallel two-dimensional (2D) imaging subproblems, which are much simpler to solve by a deep neural network. We validated our approach on simulated and experimental data.