Quantization artifact detection and removal in images

In this age of rapid development of science and technology, people get information in various ways, from computers, from mobile phones, from books. Among them, image as an important carrier of information transmission. Its importance in people's life is self-evident. Image distortion caused by...

Full description

Saved in:
Bibliographic Details
Main Author: Song, Xinyi
Other Authors: Tay Wee Peng
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2023
Subjects:
Online Access:https://hdl.handle.net/10356/168901
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-168901
record_format dspace
spelling sg-ntu-dr.10356-1689012023-07-28T15:43:48Z Quantization artifact detection and removal in images Song, Xinyi Tay Wee Peng School of Electrical and Electronic Engineering wptay@ntu.edu.sg Engineering::Electrical and electronic engineering::Electronic systems::Signal processing In this age of rapid development of science and technology, people get information in various ways, from computers, from mobile phones, from books. Among them, image as an important carrier of information transmission. Its importance in people's life is self-evident. Image distortion caused by image folding is a new research field. After being repaired by the Markov random field method, the image still has distortion. This dissertation mainly studies deep learning methods and traditional image processing methods to further solve image distortion, which involves relevant theoretical knowledge in the field of image super resolution, including convolutional neural networks, residual networks, dense networks and various classical image super resolution networks. In this dissertation, the VDSR model is modified and a good result is obtained. Master of Science (Signal Processing) 2023-06-21T08:45:16Z 2023-06-21T08:45:16Z 2023 Thesis-Master by Coursework Song, X. (2023). Quantization artifact detection and removal in images. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/168901 https://hdl.handle.net/10356/168901 en ISM-DISS-02988 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::Electronic systems::Signal processing
spellingShingle Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
Song, Xinyi
Quantization artifact detection and removal in images
description In this age of rapid development of science and technology, people get information in various ways, from computers, from mobile phones, from books. Among them, image as an important carrier of information transmission. Its importance in people's life is self-evident. Image distortion caused by image folding is a new research field. After being repaired by the Markov random field method, the image still has distortion. This dissertation mainly studies deep learning methods and traditional image processing methods to further solve image distortion, which involves relevant theoretical knowledge in the field of image super resolution, including convolutional neural networks, residual networks, dense networks and various classical image super resolution networks. In this dissertation, the VDSR model is modified and a good result is obtained.
author2 Tay Wee Peng
author_facet Tay Wee Peng
Song, Xinyi
format Thesis-Master by Coursework
author Song, Xinyi
author_sort Song, Xinyi
title Quantization artifact detection and removal in images
title_short Quantization artifact detection and removal in images
title_full Quantization artifact detection and removal in images
title_fullStr Quantization artifact detection and removal in images
title_full_unstemmed Quantization artifact detection and removal in images
title_sort quantization artifact detection and removal in images
publisher Nanyang Technological University
publishDate 2023
url https://hdl.handle.net/10356/168901
_version_ 1773551202023243776