A Closer Look at Reference Learning for Fourier Phase Retrieval

Last update: Oct 28, 2021

Related tags

Overview

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.

|-- 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.

A Closer Look at Reference Learning for Fourier Phase Retrieval

Related tags

Overview

A Closer Look at Reference Learning for Fourier Phase Retrieval

Contents

Requirements

References

Owner

Tobias Uelwer

Official repository of ICCV21 paper "Viewpoint Invariant Dense Matching for Visual Geolocalization"

Generative Modelling of BRDF Textures from Flash Images [SIGGRAPH Asia, 2021]

[WACV21] Code for our paper: Samuel, Atzmon and Chechik, "From Generalized zero-shot learning to long-tail with class descriptors"

Pipeline for employing a Lightweight deep learning models for LOW-power systems

Deep Learning Based EDM Subgenre Classification using Mel-Spectrogram and Tempogram Features"

DROPO: Sim-to-Real Transfer with Offline Domain Randomization

Neural network pruning for finding a sparse computational model for controlling a biological motor task.

Parallel Latent Tree-Induction for Faster Sequence Encoding

Recursive Bayesian Networks

Deep Surface Reconstruction from Point Clouds with Visibility Information

Face Detection and Alignment using Multi-task Cascaded Convolutional Networks (MTCNN)

Official PyTorch implementation of Joint Object Detection and Multi-Object Tracking with Graph Neural Networks

Sign Language Transformers (CVPR'20)

RMTD: Robust Moving Target Defence Against False Data Injection Attacks in Power Grids

Equivariant CNNs for the sphere and SO(3) implemented in PyTorch

Code for Environment Inference for Invariant Learning (ICML 2020 UDL Workshop Paper)

Code accompanying the NeurIPS 2021 paper "Generating High-Quality Explanations for Navigation in Partially-Revealed Environments"

Torch code for our CVPR 2018 paper "Residual Dense Network for Image Super-Resolution" (Spotlight)

Simple, but essential Bayesian optimization package

A Sign Language detection project using Mediapipe landmark detection and Tensorflow LSTM's