Modulo video recovery with deep learning

Modulo images are a particular class of images captured by modulo cameras that enable the recovery of theoretically infinite dynamic range images. The basic principle of this image is to perform a modulo operation on values that exceed the maximum dynamic range of the image. Thus, within the overexp...

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Main Author: Li,Zike
Other Authors: Tay Wee Peng
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2024
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Online Access:https://hdl.handle.net/10356/173523
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1735232024-02-16T15:42:51Z Modulo video recovery with deep learning Li,Zike Tay Wee Peng School of Electrical and Electronic Engineering wptay@ntu.edu.sg Computer and Information Science Modulo images are a particular class of images captured by modulo cameras that enable the recovery of theoretically infinite dynamic range images. The basic principle of this image is to perform a modulo operation on values that exceed the maximum dynamic range of the image. Thus, within the overexposure region of a conventional image, the modulo image can still retain some of the information of the image. Traditional modulo image restoration methods based on Markov random fields have problems such as poor restoration results and high operating costs. The method based on deep learning neural networks can achieve better recovery results. Still, the technique has some redundancy in the processing of modulo video, making it challenging to recover many modulo video frames efficiently. In this dissertation, we combine deep learning and optical flow methods to propose an architecture capable of reconstructing HDR videos from modulo videos. It has been proved experimentally that the architecture has a robust unwrapping effect and running speed. Master's degree 2024-02-14T03:02:51Z 2024-02-14T03:02:51Z 2024 Thesis-Master by Coursework Li, Z. (2024). Modulo video recovery with deep learning. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/173523 https://hdl.handle.net/10356/173523 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 Computer and Information Science
spellingShingle Computer and Information Science
Li,Zike
Modulo video recovery with deep learning
description Modulo images are a particular class of images captured by modulo cameras that enable the recovery of theoretically infinite dynamic range images. The basic principle of this image is to perform a modulo operation on values that exceed the maximum dynamic range of the image. Thus, within the overexposure region of a conventional image, the modulo image can still retain some of the information of the image. Traditional modulo image restoration methods based on Markov random fields have problems such as poor restoration results and high operating costs. The method based on deep learning neural networks can achieve better recovery results. Still, the technique has some redundancy in the processing of modulo video, making it challenging to recover many modulo video frames efficiently. In this dissertation, we combine deep learning and optical flow methods to propose an architecture capable of reconstructing HDR videos from modulo videos. It has been proved experimentally that the architecture has a robust unwrapping effect and running speed.
author2 Tay Wee Peng
author_facet Tay Wee Peng
Li,Zike
format Thesis-Master by Coursework
author Li,Zike
author_sort Li,Zike
title Modulo video recovery with deep learning
title_short Modulo video recovery with deep learning
title_full Modulo video recovery with deep learning
title_fullStr Modulo video recovery with deep learning
title_full_unstemmed Modulo video recovery with deep learning
title_sort modulo video recovery with deep learning
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/173523
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