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5 March 2021 Scalable image reconstruction in optical tomography using deep priors
Zihui Wu, Yu Sun, Alex Matlock, Lei Tian, Ulugbek S. Kamilov
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Proceedings Volume 11653, Quantitative Phase Imaging VII; 116530X (2021) https://doi.org/10.1117/12.2581361
Event: SPIE BiOS, 2021, Online Only
Abstract
Two features desired in 3D optical tomographic image reconstruction algorithm are the ability to reduce imaging artifacts and to do fast processing of large data volumes. Traditional iterative 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. SIMBA easily scales to very large 3D tomographic datasets by processing only a small subset of measurements at each iteration. We validate SIMBA on intensity diffraction tomography (IDT). Our results show that SIMBA can significantly reduce the computational burden of 3D image formation without sacrificing the imaging quality.
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Zihui Wu, Yu Sun, Alex Matlock, Lei Tian, and Ulugbek S. Kamilov "Scalable image reconstruction in optical tomography using deep priors", Proc. SPIE 11653, Quantitative Phase Imaging VII, 116530X (5 March 2021); https://doi.org/10.1117/12.2581361
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KEYWORDS
Image restoration
Optical tomography
Tomography
3D image processing
Imaging systems
Reconstruction algorithms
3D metrology
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