Deep learning for image processing and restoration

Image restoration has always been an ill-posed process due to the information loss. Some degradation can easily be simulated using mathematical formula. This simulation helps training data can be achieved at low cost. However, degradation as shadow is impossible to explicitly simulate by computer pr...

Full description

Saved in:
Bibliographic Details
Main Author: Le, Ky Nam
Other Authors: Wen Bihan
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2023
Subjects:
Online Access:https://hdl.handle.net/10356/166645
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-166645
record_format dspace
spelling sg-ntu-dr.10356-1666452023-07-07T16:59:59Z Deep learning for image processing and restoration Le, Ky Nam Wen Bihan School of Electrical and Electronic Engineering bihan.wen@ntu.edu.sg Engineering::Electrical and electronic engineering Image restoration has always been an ill-posed process due to the information loss. Some degradation can easily be simulated using mathematical formula. This simulation helps training data can be achieved at low cost. However, degradation as shadow is impossible to explicitly simulate by computer program. This makes dataset in the field of shadow removal become limited. This project aims to solve the shadow removal problem with low-cost dataset using deep learning methods. In this project, MaskshadowGAN unpaired shadow removal model is improved by equipping additional process to the original pipeline. Moreover, a method to attack BDRAR shadow detection model is discovered while experimenting a new unsupervised shadow removal pipeline. Finally, an application is developed for the users to interact with the shadow removal model. Bachelor of Engineering (Electrical and Electronic Engineering) 2023-05-09T01:34:34Z 2023-05-09T01:34:34Z 2023 Final Year Project (FYP) Le, K. N. (2023). Deep learning for image processing and restoration. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/166645 https://hdl.handle.net/10356/166645 en A3248-221 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::Electrical and electronic engineering
spellingShingle Engineering::Electrical and electronic engineering
Le, Ky Nam
Deep learning for image processing and restoration
description Image restoration has always been an ill-posed process due to the information loss. Some degradation can easily be simulated using mathematical formula. This simulation helps training data can be achieved at low cost. However, degradation as shadow is impossible to explicitly simulate by computer program. This makes dataset in the field of shadow removal become limited. This project aims to solve the shadow removal problem with low-cost dataset using deep learning methods. In this project, MaskshadowGAN unpaired shadow removal model is improved by equipping additional process to the original pipeline. Moreover, a method to attack BDRAR shadow detection model is discovered while experimenting a new unsupervised shadow removal pipeline. Finally, an application is developed for the users to interact with the shadow removal model.
author2 Wen Bihan
author_facet Wen Bihan
Le, Ky Nam
format Final Year Project
author Le, Ky Nam
author_sort Le, Ky Nam
title Deep learning for image processing and restoration
title_short Deep learning for image processing and restoration
title_full Deep learning for image processing and restoration
title_fullStr Deep learning for image processing and restoration
title_full_unstemmed Deep learning for image processing and restoration
title_sort deep learning for image processing and restoration
publisher Nanyang Technological University
publishDate 2023
url https://hdl.handle.net/10356/166645
_version_ 1772825492948254720