Python implementation of paper: Transfer-Function Approach to Substrate-Enhanced Diffraction Tomography.
If you find this project is useful in your research, please consider citing our papers:
Forward and backward scattering provide complementary volumetric and interfacial information, yet conventional three-dimensional (3D) imaging typically accesses only one. We present a substrate-enhanced diffraction tomography approach that simultaneously recovers both channels under multi-angle epi-illumination. This geometry captures one forward- and two backward-scattering bands in axially symmetric Fourier regions, where their complementary coverage enables phase–absorption separation in a non-Hermitian spectrum. Explicit 3D transfer functions are derived for both channels, and an axial Kramers–Kronig relation is established to incorporate substrate-induced boundary conditions in a unified framework. Our results establish a label-free, high-resolution 3D imaging modality that surpasses the limits of existing methods.
You can download dataset from here.
After download the dataset, put it under "./main/Data/" directory and run "mainCell_rIDT_linear_SimRecon.py" or "mainCelegans_rIDT_linear_Crop.py".
Python 3.11
NumPy 1.26
PyTorch 2.3
SciPy 1.13
This project is licensed under the terms of the MIT license. see the LICENSE file for details