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
Semi-supervised Transfer Learning for Image Rain Removal. In CVPR 2019.

Semi-supervised Transfer Learning for Image Rain Removal This package contains the Python implementation of "Semi-supervised Transfer Learning for Ima

Wei Wei 59 Dec 26, 2022
Unofficial implementation of "TTNet: Real-time temporal and spatial video analysis of table tennis" (CVPR 2020)

TTNet-Pytorch The implementation for the paper "TTNet: Real-time temporal and spatial video analysis of table tennis" An introduction of the project c

Nguyen Mau Dung 438 Dec 29, 2022
Madanalysis5 - A package for event file analysis and recasting of LHC results

Welcome to MadAnalysis 5 Outline What is MadAnalysis 5? Requirements Downloading

MadAnalysis 15 Jan 01, 2023
上海交通大学全自动抢课脚本,支持准点开抢与抢课后持续捡漏两种模式。2021/06/08更新。

Welcome to Course-Bullying-in-SJTU-v3.1! 2021/6/8 紧急更新v3.1 更新说明 为了更好地保护用户隐私,将原来用户名+密码的登录方式改为微信扫二维码+cookie登录方式,不再需要配置使用pytesseract。在使用扫码登录模式时,请稍等,二维码将马

87 Sep 13, 2022
Image Captioning on google cloud platform based on iot

Image-Captioning-on-google-cloud-platform-based-on-iot - Image Captioning on google cloud platform based on iot

Shweta_kumawat 1 Jan 20, 2022
PyTorch META-DATASET (Few-shot classification benchmark)

PyTorch META-DATASET (Few-shot classification benchmark) This repo contains a PyTorch implementation of meta-dataset and a unified implementation of s

Malik Boudiaf 39 Oct 31, 2022
Collections for the lasted paper about multi-view clustering methods (papers, codes)

Multi-View Clustering Papers Collections for the lasted paper about multi-view clustering methods (papers, codes). There also exists some repositories

Andrew Guan 10 Sep 20, 2022
Anomaly Detection Based on Hierarchical Clustering of Mobile Robot Data

We proposed a new approach to detect anomalies of mobile robot data. We investigate each data seperately with two clustering method hierarchical and k-means. There are two sub-method that we used for

Zekeriyya Demirci 1 Jan 09, 2022
FFCV: Fast Forward Computer Vision (and other ML workloads!)

Fast Forward Computer Vision: train models at a fraction of the cost with accele

FFCV 2.3k Jan 03, 2023
A python library for highly configurable transformers - easing model architecture search and experimentation.

A python library for highly configurable transformers - easing model architecture search and experimentation.

Anthony Fuller 51 Nov 20, 2022
3D Generative Adversarial Network

Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling This repository contains pre-trained models and sampling

Chengkai Zhang 791 Dec 20, 2022
[CVPR 2021] Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

[CVPR 2021] Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

Fudan Zhang Vision Group 897 Jan 05, 2023
Minimal But Practical Image Classifier Pipline Using Pytorch, Finetune on ResNet18, Got 99% Accuracy on Own Small Datasets.

PyTorch Image Classifier Updates As for many users request, I released a new version of standared pytorch immage classification example at here: http:

JinTian 106 Nov 06, 2022
NeuralDiff: Segmenting 3D objects that move in egocentric videos

NeuralDiff: Segmenting 3D objects that move in egocentric videos Project Page | Paper + Supplementary | Video About This repository contains the offic

Vadim Tschernezki 14 Dec 05, 2022
In this project we use both Resnet and Self-attention layer for cat, dog and flower classification.

cdf_att_classification classes = {0: 'cat', 1: 'dog', 2: 'flower'} In this project we use both Resnet and Self-attention layer for cdf-Classification.

3 Nov 23, 2022
A deep learning model for style-specific music generation.

DeepJ: A model for style-specific music generation https://arxiv.org/abs/1801.00887 Abstract Recent advances in deep neural networks have enabled algo

Henry Mao 704 Nov 23, 2022
Atif Hassan 103 Dec 14, 2022
Multi-objective constrained optimization for energy applications via tree ensembles

Multi-objective constrained optimization for energy applications via tree ensembles

C⚙G - Imperial College London 1 Nov 19, 2021
Self-labelling via simultaneous clustering and representation learning. (ICLR 2020)

Self-labelling via simultaneous clustering and representation learning 🆗 🆗 🎉 NEW models (20th August 2020): Added standard SeLa pretrained torchvis

Yuki M. Asano 469 Jan 02, 2023