# DeepRegularizer: Rapid Resolution Enhancement of Tomographic Imaging using Deep Learning

arXiv: https://arxiv.org/abs/2009.13777 · HTML: https://mlsanity.com/paper/2009.13777/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2020.

## Built on by (most cited first, 4 of 4)

- [On the use of deep learning for phase recovery](https://mlsanity.com/paper/2308.00942/) (2023)
- [Missing Cone Artifacts Removal in ODT using Unsupervised Deep Learning in Projection Domain](https://mlsanity.com/paper/2103.09022/) (2021)
- [AI-driven projection tomography with multicore fibre-optic cell rotation](https://mlsanity.com/paper/2312.07631/) (2023)
- [Pose-Free 3D Quantitative Phase Imaging of Flowing Cellular Populations](https://mlsanity.com/paper/2509.04848/) (2025)

## Abstract (arXiv)

Optical diffraction tomography measures the three-dimensional refractive index map of a specimen and visualizes biochemical phenomena at the nanoscale in a non-destructive manner. One major drawback of optical diffraction tomography is poor axial resolution due to limited access to the three-dimensional optical transfer function. This missing cone problem has been addressed through regularization algorithms that use a priori information, such as non-negativity and sample smoothness. However, the iterative nature of these algorithms and their parameter dependency make real-time visualization impossible. In this article, we propose and experimentally demonstrate a deep neural network, which we term DeepRegularizer, that rapidly improves the resolution of a three-dimensional refractive index map. Trained with pairs of datasets (a raw refractive index tomogram and a resolution-enhanced refractive index tomogram via the iterative total variation algorithm), the three-dimensional U-net-based convolutional neural network learns a transformation between the two tomogram domains. The feasibility and generalizability of our network are demonstrated using bacterial cells and a human leukaemic cell line, and by validating the model across different samples. DeepRegularizer offers more than an order of magnitude faster regularization performance compared to the conventional iterative method. We envision that the proposed data-driven approach can bypass the high time complexity of various image reconstructions in other imaging modalities.