3D snapshot microscopy enables fast volumetric imaging by capturing a 3D volume in a single 2D camera image, and has found a variety of biological applications such as whole brain imaging of fast neural activity in larval zebrafish. The optimal microscope design for this optical 3D-to-2D encoding is both sample- and task-dependent, with no general solution known. Highly programmable optical elements create new possibilities for sample-specific computational optimization of microscope parameters, e.g. tuning the collection of light for a given sample structure. We perform such optimization with deep learning, using a differentiable wave-optics simulation of light propagation through a programmable microscope and a neural network to reconstruct volumes from the microscope image. We introduce a class of global kernel Fourier convolutional neural networks which can efficiently decode information from multiple depths in the volume, globally encoded across a 3D snapshot image. We show that our proposed networks succeed in large field of view volume reconstruction and microscope parameter optimization where traditional networks fail. We also show that our networks outperform the state-of-the-art learned reconstruction algorithms for lensless computational photography.

翻译：3D快照显微镜通过在单一的 2D 相机图像中捕捉到3D 体积,使快速体积成像能够快速体积成像,并发现各种生物应用,如在幼子斑鱼中对快速神经活动进行整个脑成像。光学 3D-2D 编码的最佳显微镜设计是抽样和任务性的,没有一般的解决方案。高度可编程的光学元素为微镜参数的抽样特定计算优化创造了新的可能性,例如为特定样本结构调整光谱的收集。我们通过深层学习来进行这种优化,我们通过可编程显微镜和神经网络对光传播进行不同波光学模拟,以从显微镜图像中重建体积。我们引入了一类全球四层共振动神经网络,可以有效地从体积的多个深度中解码信息,全球编码在3D 光谱图像中。我们提议的网络在大型的量重建领域和微镜谱参数优化领域成功进行。我们还显示我们的网络超越了对传统网络进行不光学的状态分析的镜像学重建的镜像学镜片。