Arbitrary Image Reinflation: A deep learning technique for recovering 3D photoproduct distributions from a single 2D projection

Chris Sparling ^1,‡ , Alice Ruget ^1,‡ , Jonathan Leach ¹ and Dave Townsend ^1,2

¹ Institute of Photonics & Quantum Sciences, Heriot-Watt University, Edinburgh, EH14 4AS, UK

² Institute of Chemical Sciences, Heriot-Watt University, Edinburgh, EH14 4AS, UK

^‡ These authors contributed equally to this work

I. Dependencies

Python 3.8.11 Tensorflow 2.4.1 Keras 2.4.0

II. Training Dataset

Create_dataset.py is used to simulate different 3D distributions I_3D and their corresponding 2D projections I_2D_proj.

1. Fill the saving path save_path in create_dataset.py
2. Adjust the different parameters
3. Run create_dataset.py

III. Network

AIR.py is used to train and test the network.

1. Train your own network

After creating the dataset you can train the network by pick case = 'train' in AIR.py and specifying the path of the training dataset in save_path.

2. Reproduce the examples of the paper

We provide the checkpoint and the data for three different scenarios of the paper at the DOI address: 10.17861/1b0da270-4812-476b-9226-43e6467792c6.

1. In AIR.py, pick case = 'A' for the result of IV. B. Simulated Data: (1 + 1) Parallel Polarization Geometry. (Figure 6)
2. In AIR.py, pick case = 'B' for the result of IV. C. Experimental Data: (2 + 1) REMPI of α-Pinene. (Figure 8)
3. In AIR.py, pick case = 'B' for the result of IV. D. Simulated Data: (1 + 1) Orthogonal Polarization Geometry (Figure 12)

The results are saved respectively in Figure_6_prediction.mat, Figure_8_prediction.mat, Figure_12_prediction.mat.

3. Plot the results

WMIisosurf.m is used to plot the results. For the figures of the paper, we used a contrast cont of 1 and the shape 'half'.