Two features desired in a three-dimensional (3D) optical tomographic image reconstruction algorithm are the ability to reduce imaging artifacts and to do fast processing of large data volumes. Traditional iterative inversion algorithms are impractical in this context due to their heavy computational and memory requirements. We propose and experimentally validate a novel scalable iterative minibatch algorithm (SIMBA) for fast and high-quality optical tomographic imaging. SIMBA enables high-quality imaging by combining two complementary information sources: the physics of the imaging system characterized by its forward model and the imaging prior characterized by a denoising deep neural net. SIMBA easily scales to very large 3D tomographic datasets by processing only a small subset of measurements at each iteration. We establish the theoretical fixed-point convergence of SIMBA under nonexpansive denoisers for convex data-fidelity terms. We validate SIMBA on both simulated and experimentally collected intensity diffraction tomography (IDT) datasets. Our results show that SIMBA can significantly reduce the computational burden of 3D image formation without sacrificing the imaging quality.

中文翻译：

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SIMBA：使用深度去噪先验的光学层析成像中的可扩展反演

三维 (3D) 光学断层扫描图像重建算法中所需的两个特征是减少成像伪影和快速处理大量数据的能力。传统的迭代反演算法在这种情况下是不切实际的，因为它们需要大量的计算和内存。我们提出并通过实验验证了一种新颖的可扩展迭代小批量算法（SIMBA），用于快速和高质量的光学断层成像。SIMBA 通过结合两个互补的信息源来实现高质量的成像：以前向模型为特征的成像系统的物理特性和以深度神经网络去噪为特征的成像先验。通过在每次迭代中仅处理一小部分测量值，SIMBA 可以轻松扩展到非常大的 3D 断层摄影数据集。我们在非膨胀降噪器下为凸数据保真度项建立了 SIMBA 的理论定点收敛。我们在模拟和实验收集的强度衍射断层扫描 (IDT) 数据集上验证了 SIMBA。我们的结果表明，SIMBA 可以在不牺牲成像质量的情况下显着减少 3D 图像形成的计算负担。