SIMBA: scalable inversion in optical tomography using deep denoising priors
Files
First author draft
Date
2019-11-29
DOI
Authors
Wu, Zihui
Sun, Yu
Matlock, Alex
Liu, Jiaming
Tian, Lei
Kamilov, Ulugbek
Version
OA Version
First author draft
Citation
Zihui Wu, Yu Sun, Alex Matlock, Jiaming Liu, Lei Tian, Ulugbek Kamilov. 2019. "SIMBA: Scalable Inversion in Optical Tomography using Deep Denoising Priors." arXiv:1911.13241,
Abstract
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 mini-batch algorithm (SIMBA) for fast and high-quality optical tomographic imaging. SIMBA enables highquality 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 fixedpoint 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.