论文标题
3D散射层析成像通过深度学习，量身定制为云场
3D Scattering Tomography by Deep Learning with Architecture Tailored to Cloud Fields
论文作者
论文摘要
我们提出了3DEEPCT，这是一种用于计算机断层扫描的深神经网络，该网络从多视图图像中执行散射体积的3D重建。我们的建筑由大气云场的固定性决定。体积散射断层扫描的任务旨在从其2D预测中恢复体积。基于信号处理和物理模型，已经对这个问题进行了广泛的研究，导致了不同的逆方法。但是，这种技术通常是迭代的，表现出较高的计算负载和较长的收敛时间。我们表明，在准确性方面，3DeepCT优于基于物理的反向散射方法，并提供了显着的计算时间数量级改善。为了进一步提高恢复准确性，我们引入了结合3深C和基于物理学方法的混合模型。最终的混合动力技术享有快速推理时间和提高恢复性能。
We present 3DeepCT, a deep neural network for computed tomography, which performs 3D reconstruction of scattering volumes from multi-view images. Our architecture is dictated by the stationary nature of atmospheric cloud fields. The task of volumetric scattering tomography aims at recovering a volume from its 2D projections. This problem has been studied extensively, leading, to diverse inverse methods based on signal processing and physics models. However, such techniques are typically iterative, exhibiting high computational load and long convergence time. We show that 3DeepCT outperforms physics-based inverse scattering methods in term of accuracy as well as offering a significant orders of magnitude improvement in computational time. To further improve the recovery accuracy, we introduce a hybrid model that combines 3DeepCT and physics-based method. The resultant hybrid technique enjoys fast inference time and improved recovery performance.
