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
This repo generates the training data and the model for Morpheus-Deblend

Morpheus-Deblend This repo generates the training data and the model for Morpheus-Deblend. This is the active development repo for the project and as

Ryan Hausen 2 Apr 18, 2022
High-Resolution Image Synthesis with Latent Diffusion Models

Latent Diffusion Models Requirements A suitable conda environment named ldm can be created and activated with: conda env create -f environment.yaml co

CompVis Heidelberg 5.6k Jan 04, 2023
Landmarks Recogntion Web application using Streamlit.

Landmark Recognition Web-App using Streamlit Watch Tutorial for this project Source Trained model landmarks_classifier_asia_V1/1 is taken from the Ten

Kushal Bhavsar 5 Dec 12, 2022
Implementation of Ag-Grid component for Streamlit

streamlit-aggrid AgGrid is an awsome grid for web frontend. More information in https://www.ag-grid.com/. Consider purchasing a license from Ag-Grid i

Pablo Fonseca 556 Dec 31, 2022
3D AffordanceNet is a 3D point cloud benchmark consisting of 23k shapes from 23 semantic object categories, annotated with 56k affordance annotations and covering 18 visual affordance categories.

3D AffordanceNet This repository is the official experiment implementation of 3D AffordanceNet benchmark. 3D AffordanceNet is a 3D point cloud benchma

49 Dec 01, 2022
Red Team tool for exfiltrating files from a target's Google Drive that you have access to, via Google's API.

GD-Thief Red Team tool for exfiltrating files from a target's Google Drive that you(the attacker) has access to, via the Google Drive API. This includ

Antonio Piazza 39 Dec 27, 2022
UpChecker is a simple opensource project to host it fast on your server and check is server up, view statistic, get messages if it is down. UpChecker - just run file and use project easy

UpChecker UpChecker is a simple opensource project to host it fast on your server and check is server up, view statistic, get messages if it is down.

Yan 4 Apr 07, 2022
Using image super resolution models with vapoursynth and speeding them up with TensorRT

vs-RealEsrganAnime-tensorrt-docker Using image super resolution models with vapoursynth and speeding them up with TensorRT. Also a docker image since

4 Aug 23, 2022
Python lib to talk to pylontech lithium batteries (US2000, US3000, ...) using RS485

python-pylontech Python lib to talk to pylontech lithium batteries (US2000, US3000, ...) using RS485 What is this lib ? This lib is meant to talk to P

Frank 26 Dec 28, 2022
Official Pytorch implementation of Meta Internal Learning

Official Pytorch implementation of Meta Internal Learning

10 Aug 24, 2022
Mining-the-Social-Web-3rd-Edition - The official online compendium for Mining the Social Web, 3rd Edition (O'Reilly, 2018)

Mining the Social Web, 3rd Edition The official code repository for Mining the Social Web, 3rd Edition (O'Reilly, 2019). The book is available from Am

Mikhail Klassen 838 Jan 01, 2023
TRIQ implementation

TRIQ Implementation TF-Keras implementation of TRIQ as described in Transformer for Image Quality Assessment. Installation Clone this repository. Inst

Junyong You 115 Dec 30, 2022
TSIT: A Simple and Versatile Framework for Image-to-Image Translation

TSIT: A Simple and Versatile Framework for Image-to-Image Translation This repository provides the official PyTorch implementation for the following p

Liming Jiang 255 Nov 23, 2022
Official PyTorch implementation of GDWCT (CVPR 2019, oral)

This repository provides the official code of GDWCT, and it is written in PyTorch. Paper Image-to-Image Translation via Group-wise Deep Whitening-and-

WonwoongCho 135 Dec 02, 2022
A universal framework for learning timestamp-level representations of time series

TS2Vec This repository contains the official implementation for the paper Learning Timestamp-Level Representations for Time Series with Hierarchical C

Zhihan Yue 284 Dec 30, 2022
OMAMO: orthology-based model organism selection

OMAMO: orthology-based model organism selection OMAMO is a tool that suggests the best model organism to study a biological process based on orthologo

Dessimoz Lab 5 Apr 22, 2022
Codebase of deep learning models for inferring stability of mRNA molecules

Kaggle OpenVaccine Models Codebase of deep learning models for inferring stability of mRNA molecules, corresponding to the Kaggle Open Vaccine Challen

Eternagame 40 Dec 29, 2022
Pytorch Implementation of the paper "Cross-domain Correspondence Learning for Exemplar-based Image Translation"

CoCosNet Pytorch Implementation of the paper "Cross-domain Correspondence Learning for Exemplar-based Image Translation" (CVPR 2020 oral). Update: 202

Lingbo Yang 38 Sep 22, 2021
Koç University deep learning framework.

Knet Knet (pronounced "kay-net") is the Koç University deep learning framework implemented in Julia by Deniz Yuret and collaborators. It supports GPU

1.4k Dec 31, 2022