Split-step non-paraxial beam propagation simulation package
Project description
SSNP
Split-step non-paraxial beam propagation method
Features
- Forward model calculation based on CUDA
- Read/write data of common file type
- Gradient calculation
- Image reconstruction with regularization
Reference
Sharma, A., & Agrawal, A. (2004). Split-step non-paraxial beam propagation method. Physics and Simulation of Optoelectronic Devices XII, 5349, 132. https://doi.org/10.1117/12.528172
Lim, J., Ayoub, A. B., Antoine, E. E., & Psaltis, D. (2019). High-fidelity optical diffraction tomography of multiple scattering samples. Light: Science & Applications, 8(1), 82. https://doi.org/10.1038/s41377-019-0195-1
Changelog
0.0.2 (developing)
- Add skcuda as a substitution of reikna
- Enable to compute several illumination on the same object in a batch
- Support background refractive index (n0 > 1) in model and gradient computation
- Record partial intermediate results to effectively save memory when tracking operations
- Add stream parameter for async operation and related small tools
- Add cache to avoid unnecessary recompiling
- Fix errors:
- data.write: fix unexpectedly changing input data when scaling before write
0.0.1 (Sep 7, 2020)
- Add gradient calculation support (tracking operations and doing autograd)
- Add config to set package-wise constant
- Add Multiplier class to generate auto-cached numpy/gpu array
- Add MATLAB .mat file read & write support (rely on scipy lib)
Project details
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