SIMBA: Scalable Inversion in Optical Tomography using Deep Denoising Priors

Understand

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 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.