SIMBA: Scalable Inversion in Optical Tomography Using Deep Denoising Priors
SIMBA: Scalable Inversion in Optical Tomography Using Deep Denoising Priors
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DOI：
10.1109/jstsp.2020.2999820
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发表时间：
2019-11
影响因子：
7.5
通讯作者：
Zihui Wu;Yu Sun;Alex Matlock;Jiaming Liu;L. Tian;U. Kamilov
中科院分区：
文献类型：
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作者：
Zihui Wu;Yu Sun;Alex Matlock;Jiaming Liu;L. Tian;U. Kamilov
在三维（3D）光学断层图像重建算法中所需的两个功能是减少成像伪像并快速处理大型数据量的能力。在这种情况下，由于其大量的计算和记忆要求，在这种情况下，传统的迭代算法是不切实际的。我们提出并通过实验验证了一种新型可扩展的迭代迷你算法（SIMBA），以快速和高质量的光学层析成像。 Simba通过结合两个互补信息来源来实现高质量的成像：成像系统的物理学，其特征在于其正向模型和先前的成像，其特征是具有深层神经网的特征。 Simba通过在每次迭代时仅处理一小部分测量值来轻松缩放到非常大的3D断层扫描数据集。我们在非专业DeNoisers下为凸数据限制项建立了Simba的理论定义点收敛。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SIMBA。我们的结果表明，Simba可以显着减少3D图像形成的计算负担，而无需牺牲成像质量。
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.