Please use this identifier to cite or link to this item:
Title: Deep disentangling learning for real-world image enlightening and restoration
Authors: Chan, Yi Xuan
Keywords: Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Chan, Y. X. (2022). Deep disentangling learning for real-world image enlightening and restoration. Final Year Project (FYP), Nanyang Technological University, Singapore.
Abstract: Shadow removal is a vital image processing operation that can enlighten the illumination of shadow regions in an image. This application can elevate the accuracy and robustness of high-level computer vision tasks especially those which are heavily deep learning (DL) based (e.g., object detection, person surveillance, and vehicle tracking). Implementation of shadow removal on image or video data can mitigate the risk of unexpected fallouts in computer vision algorithms. In this Final Year Project (FYP), the prime goal is to devise a potent DL-based shadow removal algorithm that can effectively remove shadows in images without leaving any boundary trace. A comprehensive ablation study is done to investigate the effectiveness of loss functions and network modules in our proposed architecture. Quantitative and qualitative analysis show that not only our proposed model achieving comparable performance in the removal of shadow, but the final output images also have the best reconstruction image quality among the other existing shadow removal methods.
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 
Deep Disentangling Learning for Real-World Image Enlightening and Restoration .pdf
  Restricted Access
2.67 MBAdobe PDFView/Open

Page view(s)

Updated on Sep 26, 2023


Updated on Sep 26, 2023

Google ScholarTM


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