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A Closer Look at Reference Learning for Fourier Phase Retrieval

This repository contains code for our NeurIPS 2021 Workshop on Deep Learning and Inverse Problems paper.

Contents

|-- references
|   |-- gs
|   |   |-- non-oversampled
|   |   |   |-- u_cifar_gs.npy
|   |   |   |-- u_emnist_gs.npy
|   |   |   |-- u_fmnist_gs.npy
|   |   |   |-- u_mnist_gs.npy
|   |   |   `-- u_svhn_gs.npy
|   |   `-- oversampled
|   |       |-- u_cifar.npy
|   |       |-- u_emnist.npy
|   |       |-- u_fmnist.npy
|   |       |-- u_mnist.npy
|   |       `-- u_svhn.npy
|   |-- hyder
|   |   |-- non-oversampled
|   |   |   |-- u_cifar.npy
|   |   |   |-- u_emnist.npy
|   |   |   |-- u_fmnist.npy
|   |   |   |-- u_mnist.npy
|   |   |   `-- u_svhn.npy
|   |   `-- oversampled
|   |       |-- u_celeba.npy
|   |       |-- u_cifar.npy
|   |       |-- u_emnist.npy
|   |       |-- u_fmnist.npy
|   |       |-- u_mnist.npy
|   |       `-- u_svhn.npy
|   `-- random
|       |-- u_ours_noiseless.npy
|       |-- u_ours.npy
|       |-- u_random_binary.npy
|       `-- u_random.npy
|-- data.py
|-- phase-retrieval-with-reference.ipynb
|-- README.md
|-- unrolled-GS.ipynb
`-- util.py

Requirements

All experiments were conducted with the following package versions:

  • numpy==1.19.5
  • torch==1.9.0
  • torchvision==0.10.0
  • matplotlib==3.4.3
  • scikit-image==0.17.2

The reference images for the oversampled case dicussed in Hyder et al. [1] were obtained from the official repository.

References

[1] Rakib Hyder, Zikui Cai, and M Salman Asif. Solving phase retrieval with a learned reference. In European Conference on Computer Vision, pages 425–441. Springer, 2020.