Light-field microscopy (LFM) is a type of all-optical imaging system that is able to capture 4D geometric information of light rays and can reconstruct a 3D model from a single snapshot. In this paper, we propose a new 3D localization approach to effectively detect 3D positions of neuronal cells from a single light-field image with high accuracy and outstanding robustness to light scattering. This is achieved by constructing a depth-aware dictionary and by combining it with convolutional sparse coding. Specifically, our approach includes 3 key parts: light-field calibration, depth-aware dictionary construction, and localization based on convolutional sparse coding (CSC). In the first part, an observed raw light-field image is calibrated and then decoded into a two-plane parameterized 4D format which leads to the epi-polar plane image (EPI). The second part involves simulating a set of light-fields using a wave-optics forward model for a ball-shaped volume that is located at different depths. Then, a depth-aware dictionary is constructed where each element is a synthetic EPI associated to a specific depth. Finally, by taking full advantage of the sparsity prior and shift-invariance property of EPI, 3D localization is achieved via convolutional sparse coding on an observed EPI with respect to the depth-aware EPI dictionary. We evaluate our approach on both non-scattering specimen (fluorescent beads suspended in agarose gel) and scattering media (brain tissues of genetically encoded mice). Extensive experiments demonstrate that our approach can reliably detect the 3D positions of granular targets with small Root Mean Square Error (RMSE), high robustness to optical aberration and light scattering in mammalian brain tissues.

中文翻译：

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通过对极图像上的卷积稀疏编码进行光场显微镜的 3D 定位


光场显微镜（LFM）是一种全光学成像系统，能够捕获光线的 4D 几何信息，并可以从单个快照重建 3D 模型。在本文中，我们提出了一种新的 3D 定位方法，可以从单个光场图像中有效地检测神经元细胞的 3D 位置，并且具有高精度和出色的光散射鲁棒性。这是通过构建深度感知字典并将其与卷积稀疏编码相结合来实现的。具体来说，我们的方法包括3个关键部分：光场校准、深度感知字典构建和基于卷积稀疏编码（CSC）的定位。在第一部分中，对观察到的原始光场图像进行校准，然后将其解码为两平面参数化 4D 格式，从而生成外极平面图像 (EPI)。第二部分涉及使用波动光学正演模型对位于不同深度的球形体积模拟一组光场。然后，构建一个深度感知字典，其中每个元素都是与特定深度相关的合成 EPI。最后，通过充分利用EPI的稀疏先验和平移不变性，通过对观察到的EPI相对于深度感知EPI字典的卷积稀疏编码实现3D定位。我们在非散射样本（悬浮在琼脂糖凝胶中的荧光珠）和散射介质（基因编码小鼠的脑组织）上评估我们的方法。大量实验表明，我们的方法可以可靠地检测颗粒目标的 3D 位置，且均方根误差 (RMSE) 小，对哺乳动物脑组织中的光学像差和光散射具有高鲁棒性。