Awesome
Self-Holo
Diffraction model-informed neural network for unsupervised layer-based computer-generated holography.<br>
Paper
X. Shui, H. Zheng, X. Xia, F. Yang, W. Wang, and Y. Yu, “Diffraction model-informed neural network for <br> unsupervised layer-based computer-generated holography,” Opt. Express 35(25), (2022).
Dataset
The RGB-D datasets are from TensorHolography.
High-level Structure
The code is organized as follows:
./src/
train.py
trains the selfholo.dataLoader.py
loads a set of images.complex_generator.py
is the target complex_amplitude generator.holo_encoder.py
is the phase encoder.selfholo.py
is the pipeline of selfholo.propagation_ASM.py
contains the angular spectrum method.perceptualloss.py
contains mseloss and perceptualloss.predict.py
predicts 2D holograms or 3D holograms.utils.py
contains utility functions.
We recommend that the readers to experiment with different upsampling approaches or different CNN frameworks<br> to further improve the quality of the images.
Running the test
python ./src/train.py --run_id=selfholo
Ackonwledgement
We are thankful for the open source of NeuralHolography, HoloEncoder,and HoloEncoder-Pytorch-Version. These works are very helpful for our research.