Imaging through scattering media

This project addresses the challenge of imaging through scattering media, a common issue affecting various fields, from autonomous driving through fog to health diagnostics through tissue imaging. Conventional approaches, such as deconvolution and speckle correlation, have their own limitations such...

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Bibliographic Details
Main Author: Wen, Zhilan
Other Authors: Cuong Dang
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
Online Access:https://hdl.handle.net/10356/176426
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1764262024-05-17T15:44:26Z Imaging through scattering media Wen, Zhilan Cuong Dang School of Electrical and Electronic Engineering HCDang@ntu.edu.sg Engineering This project addresses the challenge of imaging through scattering media, a common issue affecting various fields, from autonomous driving through fog to health diagnostics through tissue imaging. Conventional approaches, such as deconvolution and speckle correlation, have their own limitations such as the invasive nature and time-consuming computation. Recent developed approach with machine learning (ML) offers a promising non-invasive option. It can extract important system features without knowing the detailed knowledge of the physical principles. Our work integrates machine learning with conventional methods to reconstruct scattered images, focusing on varying speckle sizes. The study is purely software-based, simulating the imaging and reconstruction process to validate the proposed methodology. We utilize a U-net model, a form of a convolutional neural network known for its effectiveness in image segmentation and denoising, to predict images from unseen point spread functions (PSFs), which demonstrates the model's ability to generalize across different scattering conditions. The results show the U-net model's potential in reconstructing images through unseen scattering media, making a significant step forward in non-invasive imaging techniques. The findings in this study can be used to accelerate the development of better ML models to reconstruct scattered images in the field of image processing. Bachelor's degree 2024-05-16T12:40:58Z 2024-05-16T12:40:58Z 2024 Final Year Project (FYP) Wen, Z. (2024). Imaging through scattering media. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/176426 https://hdl.handle.net/10356/176426 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering
spellingShingle Engineering
Wen, Zhilan
Imaging through scattering media
description This project addresses the challenge of imaging through scattering media, a common issue affecting various fields, from autonomous driving through fog to health diagnostics through tissue imaging. Conventional approaches, such as deconvolution and speckle correlation, have their own limitations such as the invasive nature and time-consuming computation. Recent developed approach with machine learning (ML) offers a promising non-invasive option. It can extract important system features without knowing the detailed knowledge of the physical principles. Our work integrates machine learning with conventional methods to reconstruct scattered images, focusing on varying speckle sizes. The study is purely software-based, simulating the imaging and reconstruction process to validate the proposed methodology. We utilize a U-net model, a form of a convolutional neural network known for its effectiveness in image segmentation and denoising, to predict images from unseen point spread functions (PSFs), which demonstrates the model's ability to generalize across different scattering conditions. The results show the U-net model's potential in reconstructing images through unseen scattering media, making a significant step forward in non-invasive imaging techniques. The findings in this study can be used to accelerate the development of better ML models to reconstruct scattered images in the field of image processing.
author2 Cuong Dang
author_facet Cuong Dang
Wen, Zhilan
format Final Year Project
author Wen, Zhilan
author_sort Wen, Zhilan
title Imaging through scattering media
title_short Imaging through scattering media
title_full Imaging through scattering media
title_fullStr Imaging through scattering media
title_full_unstemmed Imaging through scattering media
title_sort imaging through scattering media
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/176426
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