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

E-Ink Magic Calendar that automatically syncs to Google Calendar and runs off a battery powered Raspberry Pi Zero

code for Grapadora research paper experimentation

This is project is the implementation of the DeepShift: Towards Multiplication-Less Neural Networks paper

Motion planning algorithms commonly used on autonomous vehicles. (path planning + path tracking)

Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement (NeurIPS 2020)

A GUI for Face Recognition, based upon Docker, Tkinter, GPU and a camera device.

The materials used in the SaxonJS tutorial presented at Declarative Amsterdam, 2021

Code and dataset for AAAI 2021 paper FixMyPose: Pose Correctional Describing and Retrieval Hyounghun Kim, Abhay Zala, Graham Burri, Mohit Bansal.

Self-Supervised Document-to-Document Similarity Ranking via Contextualized Language Models and Hierarchical Inference

Code accompanying the paper "How Tight Can PAC-Bayes be in the Small Data Regime?"

NVIDIA container runtime

Catbird is an open source paraphrase generation toolkit based on PyTorch.

Neural network for stock price prediction

Pytorch and Keras Implementations of Hyperspectral Image Classification -- Traditional to Deep Models: A Survey for Future Prospects.

Python Single Object Tracking Evaluation

Activating More Pixels in Image Super-Resolution Transformer

Simulation-based performance analysis of server-less Blockchain-enabled Federated Learning

Experiments and code to generate the GINC small-scale in-context learning dataset from "An Explanation for In-context Learning as Implicit Bayesian Inference"

FairMOT for Multi-Class MOT using YOLOX as Detector

Official Pytorch implementation for "End2End Occluded Face Recognition by Masking Corrupted Features, TPAMI 2021"