Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/166645
Title: Deep learning for image processing and restoration
Authors: Le, Ky Nam
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2023
Publisher: Nanyang Technological University
Source: 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
Project: A3248-221
Abstract: 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.
URI: https://hdl.handle.net/10356/166645
Schools: School of Electrical and Electronic Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
Le-Ky-Nam-FYP-Final-Report.pdf
  Restricted Access
4.13 MBAdobe PDFView/Open

Page view(s)

83
Updated on Feb 21, 2024

Download(s)

15
Updated on Feb 21, 2024

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.