Computational modelling of ray propagation through optical elements using the principles of geometric optics (Ray Tracer)

Overview

Computational modelling of ray propagation through optical elements using the principles of geometric optics (Ray Tracer)

Introduction

By applying the principles of geometric optics, imaging performances of lenses were investigated via examining the propagation of optical rays through various optical systems. The optical system and its elements were modelled with an object-oriented approach using the Python programming language. Through utilising a ray bundle with specific parameters, the performances of a planoconvex lens with different orientations were analysed. The orientation with the convex surface facing the incident beam was found to be more effective at minimising the spherical aberration. This was evident from the value of the geometric RMS spot radius of 1.85 x 10^-5} m at the paraxial focus compared to 7.04 x 10^-5 m for the plano-convex orientation. This was further supported by the relatively slow rate of increase in the RMS spot radius with the beam size for the convex-plano orientation. Furthermore, by optimising the curvatures of a singlet lens with a image distance of 100 mm, the best form curvatures were approximated as 0.01417 mm^-1 and -0.00532 mm^-1 with the RMS spot radius of 6.07 x 10^-8 m, leading to a conclusion that the system was diffraction limited and the effect of diffraction was substantial when using a beam radius smaller than 13.60 mm.

Requirements

Python 2.x is required to run the scripts (except for those with name beginning with 'ODE_').

Create an environment using conda as follows:

  conda create -n python2 python=2.x

Then activate the new environment by:

  conda activate python2

Results

In an ideal case, optical rays refracting through a spherical lens can be made to converge at a single point known as the focal point. However, in practice, rays fail to converge at a single point and a blurring effect occurs. This optical effect, known as the spherical aberration, is a result of the rays propagating parallel to the optical axis through a spherical lens at different distances from the axis.$^{1, 3}$ The rays further away from the optical axis experience greater refraction and thus they intersect the optical axis slightly behind the paraxial focus before diverging (FIG. 1).

For a single lens, spherical aberration can be minimised either by changing the orientation of the lens or by carefully choosing the curvatures of the spherical surfaces into the best form. In this investigation, both cases are examined using collimated ray bundles with uniformly distributed rays of various diameters with the aim to minimise this effect.

SA Figure 1: A lens displaying spherical aberration - the marginal and paraxial rays focus at the points F_1 and F_2 respectively.


single

Figure 2: A ray bundle of radius 5 mm propagating through a single spherical surface with a curvature of 0.03 mm^-1 and refracting towards the optical axis.


spotplot2

Figure 3: The non-uniform ring pattern that is shown in the figure is symbolic of the spherical aberration effect. The aberration is significantly reduced using the convex-plano orientation.


RMSPC

Figure 4: A graph depicting the change in the RMS spot radius at the paraxial focus with increasing beam size.


RMSDL

Figure 5: A graph showing the relationships of the diffraction limit and the RMS spot radius with increasing beam size.

🔗 Links

linkedin

License

MIT License

Owner
Son Gyo Jung
Son Gyo Jung
This repository contains small projects related to Neural Networks and Deep Learning in general.

ILearnDeepLearning.py Description People say that nothing develops and teaches you like getting your hands dirty. This repository contains small proje

Piotr Skalski 1.2k Dec 22, 2022
ACV is a python library that provides explanations for any machine learning model or data.

ACV is a python library that provides explanations for any machine learning model or data. It gives local rule-based explanations for any model or data and different Shapley Values for tree-based mod

Salim Amoukou 85 Dec 27, 2022
Implementation of the paper Scalable Intervention Target Estimation in Linear Models (NeurIPS 2021), and the code to generate simulation results.

Scalable Intervention Target Estimation in Linear Models Implementation of the paper Scalable Intervention Target Estimation in Linear Models (NeurIPS

0 Oct 25, 2021
Object detection evaluation metrics using Python.

Object detection evaluation metrics using Python.

Louis Facun 2 Sep 06, 2022
POT : Python Optimal Transport

POT: Python Optimal Transport This open source Python library provide several solvers for optimization problems related to Optimal Transport for signa

Python Optimal Transport 1.7k Dec 31, 2022
Range Image-based LiDAR Localization for Autonomous Vehicles Using Mesh Maps

Range Image-based 3D LiDAR Localization This repo contains the code for our ICRA2021 paper: Range Image-based LiDAR Localization for Autonomous Vehicl

Photogrammetry & Robotics Bonn 208 Dec 15, 2022
Build Graph Nets in Tensorflow

Graph Nets library Graph Nets is DeepMind's library for building graph networks in Tensorflow and Sonnet. Contact DeepMind 5.2k Jan 05, 2023

Training and Evaluation Code for Neural Volumes

Neural Volumes This repository contains training and evaluation code for the paper Neural Volumes. The method learns a 3D volumetric representation of

Meta Research 370 Dec 08, 2022
CLADE - Efficient Semantic Image Synthesis via Class-Adaptive Normalization (TPAMI 2021)

Efficient Semantic Image Synthesis via Class-Adaptive Normalization (Accepted by TPAMI)

tzt 49 Nov 17, 2022
Visual dialog agents with pre-trained vision-and-language encoders.

Learning Better Visual Dialog Agents with Pretrained Visual-Linguistic Representation Or READ-UP: Referring Expression Agent Dialog with Unified Pretr

7 Oct 08, 2022
Efficiently computes derivatives of numpy code.

Note: Autograd is still being maintained but is no longer actively developed. The main developers (Dougal Maclaurin, David Duvenaud, Matt Johnson, and

Formerly: Harvard Intelligent Probabilistic Systems Group -- Now at Princeton 6.1k Jan 08, 2023
Official PyTorch implementation of the paper "Recycling Discriminator: Towards Opinion-Unaware Image Quality Assessment Using Wasserstein GAN", accepted to ACM MM 2021 BNI Track.

RecycleD Official PyTorch implementation of the paper "Recycling Discriminator: Towards Opinion-Unaware Image Quality Assessment Using Wasserstein GAN

Yunan Zhu 23 Nov 05, 2022
Collection of generative models in Pytorch version.

pytorch-generative-model-collections Original : [Tensorflow version] Pytorch implementation of various GANs. This repository was re-implemented with r

Hyeonwoo Kang 2.4k Dec 31, 2022
A Light CNN for Deep Face Representation with Noisy Labels

A Light CNN for Deep Face Representation with Noisy Labels Citation If you use our models, please cite the following paper: @article{wulight, title=

Alfred Xiang Wu 715 Nov 05, 2022
Sample code and notebooks for Vertex AI, the end-to-end machine learning platform on Google Cloud

Google Cloud Vertex AI Samples Welcome to the Google Cloud Vertex AI sample repository. Overview The repository contains notebooks and community conte

Google Cloud Platform 560 Dec 31, 2022
An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.

An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models. Hyperactive: is very easy to lear

Simon Blanke 422 Jan 04, 2023
Implementation of StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation in PyTorch

StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation Implementation of StyleSpace Analysis: Disentangled Controls for StyleGAN Ima

Xuanchi Ren 86 Dec 07, 2022
Motion planning environment for Sampling-based Planners

Sampling-Based Motion Planners' Testing Environment Sampling-based motion planners' testing environment (sbp-env) is a full feature framework to quick

Soraxas 23 Aug 23, 2022
EMNLP 2021: Single-dataset Experts for Multi-dataset Question-Answering

MADE (Multi-Adapter Dataset Experts) This repository contains the implementation of MADE (Multi-adapter dataset experts), which is described in the pa

Princeton Natural Language Processing 68 Jul 18, 2022
Improving the robustness and performance of biomedical NLP models through adversarial training

RobustBioNLP Improving the robustness and performance of biomedical NLP models through adversarial training In this repository you can find suppliment

Milad Moradi 3 Sep 20, 2022