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
An algorithmic trading bot that learns and adapts to new data and evolving markets using Financial Python Programming and Machine Learning.

ALgorithmic_Trading_with_ML An algorithmic trading bot that learns and adapts to new data and evolving markets using Financial Python Programming and

1 Mar 14, 2022
An official implementation of "Background-Aware Pooling and Noise-Aware Loss for Weakly-Supervised Semantic Segmentation" (CVPR 2021) in PyTorch.

BANA This is the implementation of the paper "Background-Aware Pooling and Noise-Aware Loss for Weakly-Supervised Semantic Segmentation". For more inf

CV Lab @ Yonsei University 59 Dec 12, 2022
Implementation of the paper "Shapley Explanation Networks"

Shapley Explanation Networks Implementation of the paper "Shapley Explanation Networks" at ICLR 2021. Note that this repo heavily uses the experimenta

68 Dec 27, 2022
Data-depth-inference - Data depth inference with python

Welcome! This readme will guide you through the use of the code in this reposito

Marco 3 Feb 08, 2022
An open-source outlier detection package by Getcontact Data Team

pyfbad The pyfbad library supports anomaly detection projects. An end-to-end anomaly detection application can be written using the source codes of th

Teknasyon Tech 41 Dec 27, 2022
Imposter-detector-2022 - HackED 2022 Team 3IQ - 2022 Imposter Detector

HackED 2022 Team 3IQ - 2022 Imposter Detector By Aneeljyot Alagh, Curtis Kan, Jo

Joshua Ji 3 Aug 20, 2022
City-Scale Multi-Camera Vehicle Tracking Guided by Crossroad Zones Code

City-Scale Multi-Camera Vehicle Tracking Guided by Crossroad Zones Requirements Python 3.8 or later with all requirements.txt dependencies installed,

88 Dec 12, 2022
Multiple paper open-source codes of the Microsoft Research Asia DKI group

📫 Paper Code Collection (MSRA DKI Group) This repo hosts multiple open-source codes of the Microsoft Research Asia DKI Group. You could find the corr

Microsoft 249 Jan 08, 2023
Distributed Asynchronous Hyperparameter Optimization in Python

Hyperopt: Distributed Hyperparameter Optimization Hyperopt is a Python library for serial and parallel optimization over awkward search spaces, which

6.5k Jan 01, 2023
Identify the emotion of multiple speakers in an Audio Segment

MevonAI - Speech Emotion Recognition Identify the emotion of multiple speakers in a Audio Segment Report Bug · Request Feature Try the Demo Here Table

Suyash More 110 Dec 03, 2022
PGPortfolio: Policy Gradient Portfolio, the source code of "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem"(https://arxiv.org/pdf/1706.10059.pdf).

This is the original implementation of our paper, A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem (arXiv:1706.1

Zhengyao Jiang 1.5k Dec 29, 2022
Official implementation of VQ-Diffusion

Official implementation of VQ-Diffusion: Vector Quantized Diffusion Model for Text-to-Image Synthesis

Microsoft 592 Jan 03, 2023
Official implementation of deep-multi-trajectory-based single object tracking (IEEE T-CSVT 2021).

DeepMTA_PyTorch Officical PyTorch Implementation of "Dynamic Attention-guided Multi-TrajectoryAnalysis for Single Object Tracking", Xiao Wang, Zhe Che

Xiao Wang(王逍) 7 Dec 03, 2022
A unified 3D Transformer Pipeline for visual synthesis

Overview This is the official repo for the paper: "NÜWA: Visual Synthesis Pre-training for Neural visUal World creAtion". NÜWA is a unified multimodal

Microsoft 2.6k Jan 03, 2023
The PyTorch implementation of paper REST: Debiased Social Recommendation via Reconstructing Exposure Strategies

REST The PyTorch implementation of paper REST: Debiased Social Recommendation via Reconstructing Exposure Strategies. Usage Download dataset Download

DMIRLAB 2 Mar 13, 2022
Official repository for "Deep Recurrent Neural Network with Multi-scale Bi-directional Propagation for Video Deblurring".

RNN-MBP Deep Recurrent Neural Network with Multi-scale Bi-directional Propagation for Video Deblurring (AAAI-2022) by Chao Zhu, Hang Dong, Jinshan Pan

SIV-LAB 22 Aug 31, 2022
Source code for the GPT-2 story generation models in the EMNLP 2020 paper "STORIUM: A Dataset and Evaluation Platform for Human-in-the-Loop Story Generation"

Storium GPT-2 Models This is the official repository for the GPT-2 models described in the EMNLP 2020 paper [STORIUM: A Dataset and Evaluation Platfor

Nader Akoury 27 Dec 20, 2022
code for generating data set ES-ImageNet with corresponding training code

es-imagenet-master code for generating data set ES-ImageNet with corresponding training code dataset generator some codes of ODG algorithm The variabl

Ordinarabbit 18 Dec 25, 2022
A PyTorch implementation of EventProp [https://arxiv.org/abs/2009.08378], a method to train Spiking Neural Networks

Spiking Neural Network training with EventProp This is an unofficial PyTorch implemenation of EventProp, a method to compute exact gradients for Spiki

Pedro Savarese 35 Jul 29, 2022
Lipstick ain't enough: Beyond Color-Matching for In-the-Wild Makeup Transfer (CVPR 2021)

Table of Content Introduction Datasets Getting Started Requirements Usage Example Training & Evaluation CPM: Color-Pattern Makeup Transfer CPM is a ho

VinAI Research 248 Dec 13, 2022