Speckle-free Holography with Partially Coherent Light Sources and Camera-in-the-loop Calibration

Overview

Speckle-free Holography with Partially Coherent Light Sources and Camera-in-the-loop Calibration

Project Page | Paper

Yifan Peng*, Suyeon Choi*, Jonghyun Kim, Gordon Wetzstein

* Authors contributed equally.

This repository contains the scripts associated with the Science Advances paper "Speckle-free Holography with Partially Coherent Light Sources and Camera-in-the-loop Calibration"

Getting Started

First, load the submodules in neural_holography folder with

git submodule init
git submodule update

Also, you can modify the spectrum information in spectra folder based on measured spectrum from your own setup.

High-level structure

The code is organized as follows:

  • main.py generates phase patterns with our partially coherent propagatator via SGD/CITL
  • propagation_partial.py contains the partially coherent wave propagation operator implementation.
  • spectrum.py contains utility functions for reading measured spectra.

./neural-holography/: See here for descriptions.

Running the test

The SLM phase patterns can be reproduced with

SGD with the partially coherent model:

python main.py --channel=0 --method=SGD --prop_model=model --root_path=./phases

SGD with Camera-in-the-loop optimization:

python main.py --channel=0 --method=SGD --prop_model=model --citl=True --root_path=./phases

Citation

If you find our work useful in your research, please cite:

@article{Peng:2021:PartiallyCoherent,
author = {Yifan Peng  and Suyeon Choi  and Jonghyun Kim  and Gordon Wetzstein },
title = {Speckle-free holography with partially coherent light sources and camera-in-the-loop calibration},
journal = {Science Advances},
volume = {7},
number = {46},
pages = {eabg5040},
year = {2021},
doi = {10.1126/sciadv.abg5040}

License

This project is licensed under the following license, with exception of the file "data/1.png", which is licensed under the CC-BY license.

Copyright (c) 2021, Stanford University

All rights reserved.

Redistribution and use in source and binary forms for academic and other non-commercial purposes with or without modification, are permitted provided that the following conditions are met:

  • Redistributions of source code, including modified source code, must retain the above copyright notice, this list of conditions and the following disclaimer.

  • Redistributions in binary form or a modified form of the source code must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

  • Neither the name of The Leland Stanford Junior University, any of its trademarks, the names of its employees, nor contributors to the source code may be used to endorse or promote products derived from this software without specific prior written permission.

  • Where a modified version of the source code is redistributed publicly in source or binary forms, the modified source code must be published in a freely accessible manner, or otherwise redistributed at no charge to anyone requesting a copy of the modified source code, subject to the same terms as this agreement.

THIS SOFTWARE IS PROVIDED BY THE TRUSTEES OF THE LELAND STANFORD JUNIOR UNIVERSITY "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE LELAND STANFORD JUNIOR UNIVERSITY OR ITS TRUSTEES BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

Contact

If you have any questions, please contact

Owner
Stanford Computational Imaging Lab
Next-generation computational imaging and display systems.
Stanford Computational Imaging Lab
Code release of paper Improving neural implicit surfaces geometry with patch warping

NeuralWarp: Improving neural implicit surfaces geometry with patch warping Project page | Paper Code release of paper Improving neural implicit surfac

François Darmon 167 Dec 30, 2022
CLNTM - Contrastive Learning for Neural Topic Model

Contrastive Learning for Neural Topic Model This repository contains the impleme

Thong Thanh Nguyen 25 Nov 24, 2022
PyGCL: Graph Contrastive Learning Library for PyTorch

PyGCL: Graph Contrastive Learning for PyTorch PyGCL is an open-source library for graph contrastive learning (GCL), which features modularized GCL com

GCL: Graph Contrastive Learning Library for PyTorch 594 Jan 08, 2023
The author's officially unofficial PyTorch BigGAN implementation.

BigGAN-PyTorch The author's officially unofficial PyTorch BigGAN implementation. This repo contains code for 4-8 GPU training of BigGANs from Large Sc

Andy Brock 2.6k Jan 02, 2023
Efficient semidefinite bounds for multi-label discrete graphical models.

Low rank solvers #################################### benchmark/ : folder with the random instances used in the paper. ############################

1 Dec 08, 2022
Awesome-google-colab - Google Colaboratory Notebooks and Repositories

Unofficial Google Colaboratory Notebook and Repository Gallery Please contact me to take over and revamp this repo (it gets around 30k views and 200k

Derek Snow 1.2k Jan 03, 2023
[CVPR2021] De-rendering the World's Revolutionary Artefacts

De-rendering the World's Revolutionary Artefacts Project Page | Video | Paper In CVPR 2021 Shangzhe Wu1,4, Ameesh Makadia4, Jiajun Wu2, Noah Snavely4,

49 Nov 06, 2022
CoANet: Connectivity Attention Network for Road Extraction From Satellite Imagery

CoANet: Connectivity Attention Network for Road Extraction From Satellite Imagery This paper (CoANet) has been published in IEEE TIP 2021. This code i

Jie Mei 53 Dec 03, 2022
PyTorch reimplementation of REALM and ORQA

PyTorch reimplementation of REALM and ORQA

Li-Huai (Allan) Lin 17 Aug 20, 2022
[ICLR2021] Unlearnable Examples: Making Personal Data Unexploitable

Unlearnable Examples Code for ICLR2021 Spotlight Paper "Unlearnable Examples: Making Personal Data Unexploitable " by Hanxun Huang, Xingjun Ma, Sarah

Hanxun Huang 98 Dec 07, 2022
This project aims at providing a concise, easy-to-use, modifiable reference implementation for semantic segmentation models using PyTorch.

Semantic Segmentation on PyTorch (include FCN, PSPNet, Deeplabv3, Deeplabv3+, DANet, DenseASPP, BiSeNet, EncNet, DUNet, ICNet, ENet, OCNet, CCNet, PSANet, CGNet, ESPNet, LEDNet, DFANet)

2.4k Jan 08, 2023
BEAMetrics: Benchmark to Evaluate Automatic Metrics in Natural Language Generation

BEAMetrics: Benchmark to Evaluate Automatic Metrics in Natural Language Generation Installing The Dependencies $ conda create --name beametrics python

7 Jul 04, 2022
Tutorials and implementations for "Self-normalizing networks"

Self-Normalizing Networks Tutorials and implementations for "Self-normalizing networks"(SNNs) as suggested by Klambauer et al. (arXiv pre-print). Vers

Institute of Bioinformatics, Johannes Kepler University Linz 1.6k Jan 07, 2023
This is a collection of simple PyTorch implementations of neural networks and related algorithms. These implementations are documented with explanations,

labml.ai Deep Learning Paper Implementations This is a collection of simple PyTorch implementations of neural networks and related algorithms. These i

labml.ai 16.4k Jan 09, 2023
D²Conv3D: Dynamic Dilated Convolutions for Object Segmentation in Videos

D²Conv3D: Dynamic Dilated Convolutions for Object Segmentation in Videos This repository contains the implementation for "D²Conv3D: Dynamic Dilated Co

17 Oct 20, 2022
Türkiye Canlı Mobese Görüntülerinde Profesyonel Nesne Takip Sistemi

Türkiye Mobese Görüntü Takip Türkiye Mobese görüntülerinde OPENCV ve Yolo ile takip sistemi Multiple Object Tracking System in Turkish Mobese with OPE

15 Dec 22, 2022
Title: Heart-Failure-Classification

This Notebook is based off an open source dataset available on where I have created models to classify patients who can potentially witness heart failure on the basis of various parameters. The best

Akarsh Singh 2 Sep 13, 2022
This repo is about to create the Streamlit application for given ML model.

HR-Attritiion-using-Streamlit This repo is about to create the Streamlit application for given ML model. Problem Statement: Managing peoples at workpl

Pavan Giri 0 Dec 10, 2021
Experiments for Fake News explainability project

fake-news-explainability Experiments for fake news explainability project This repository only contains the notebooks used to train the models and eva

Lorenzo Flores (Lj) 1 Dec 03, 2022
An open-access benchmark and toolbox for electricity price forecasting

epftoolbox The epftoolbox is the first open-access library for driving research in electricity price forecasting. Its main goal is to make available a

97 Dec 05, 2022