Comparison between near-field holography and far-field ptychography
This code repo is used in the following publication(s):
- Du, M., Gursoy, D., & Jacobsen, C. (2019). Near, far, wherever you are: simulations on the dose efficiency of holographic and ptychographic coherent imaging. arXiv preprint arXiv:1908.06770. https://arxiv.org/abs/1908.06770
The repository itself doesn't require installation, but you need to prepare your environment with all of its dependencies, represented by TensorFlow. Use the GPU-enabled version of TensorFlow from Anaconda is usually the most convenient approach:
conda install tensorflow-gpu
Use pip install to get other missing dependencies if any.
Use create_ptycho_data.py and create_fullfield_data.py to create simulated data for ptychography and fullfield CDI, respectively. Make sure grid_delta.npy and grid_beta.npy is contained in phantom_path (e.g., cell/ptychography/phantom) (the files are not uploaded yet; will do after my workstation is fixed), and check all parameters are set correctly. Running the scripts creates an HDF5 file at save_path that contains a dataset called exchange. The array shape is [n_angles, detector_size_y, detector_size_x] for fullfield, and [n_angles, n_spots_per_angle, detector_size_y, detector_size_x] for ptychography.
To add Poisson noise to the noise-free dataset generated in the previous step, use create_noisy_data.py. n_ph_tx specifies the number of photons hitting the sample (i.e., exclusing those hitting vacuum). n_sample_pixel gives the number of pixels in the sample's support (i.e., non-zero region). The script detect automatically whether the source dataset is ptychography or fullfield from the name of the directory.
Use reconstruct_ptycho.py for ptychography reconstruction. By specifying n_ls, the script can run reconstruction for a list of noise levels sequentially. The reconstruction function uses Gaussian (least square) loss function by default; to switch to Poisson loss function, uncomment the line of
loss = tf.reduce_mean(tf.abs(exiting_ls) ** 2 * poisson_multiplier - tf.abs(this_prj_batch[i]) ** 2 * poisson_multiplier * tf.log(tf.abs(exiting_ls) ** 2 * poisson_multiplier), name='loss')
and comment the original declaration of the loss function. Also, the line in reconstruct_ptycho.py reading
params['poisson_multiplier'] = n_ph_1 / 5e4
calculate the number of incident photons per pixel and the 5e4 is hard coded for cell. If you are using a different object, change it to the number of non-zero pixels.