Wavelet-based image and video super-resolution reconstruction.
| dc.contributor.advisor | Zhao, Yifan | |
| dc.contributor.advisor | Jenkins, Karl W. | |
| dc.contributor.author | Witwit, Wasnaa | |
| dc.date.accessioned | 2022-05-03T19:02:00Z | |
| dc.date.available | 2022-05-03T19:02:00Z | |
| dc.date.issued | 2017-12 | |
| dc.description.abstract | Super-resolution reconstruction process offers the solution to overcome the high-cost and inherent resolution limitations of current imaging systems. The wavelet transform is a powerful tool for super-resolution reconstruction. This research provides a detailed study of the wavelet-based super-resolution reconstruction process, and wavelet-based resolution enhancement process (with which it is closely associated). It was addressed to handle an explicit need for a robust wavelet-based method that guarantees efficient utilisation of the SR reconstruction problem in the wavelet-domain, which will lead to a consistent solution of this problem and improved performance. This research proposes a novel performance assessment approach to improve the performance of the existing wavelet-based image resolution enhancement techniques. The novel approach is based on identifying the factors that effectively influence on the performance of these techniques, and designing a novel optimal factor analysis (OFA) algorithm. A new wavelet-based image resolution enhancement method, based on discrete wavelet transform and new-edge directed interpolation (DWT-NEDI), and an adaptive thresholding process, has been developed. The DWT-NEDI algorithm aims to correct the geometric errors and remove the noise for degraded satellite images. A robust wavelet-based video super-resolution technique, based on global motion is developed by combining the DWT-NEDI method, with super-resolution reconstruction methods, in order to increase the spatial-resolution and remove the noise and aliasing artefacts. A new video super-resolution framework is designed using an adaptive local motion decomposition and wavelet transform reconstruction (ALMD-WTR). This is to address the challenge of the super-resolution problem for the real-world video sequences containing complex local motions. The results show that OFA approach improves the performance of the selected wavelet-based methods. The DWT-NEDI algorithm outperforms the state-of-the art wavelet-based algorithms. The global motion-based algorithm has the best performance over the super-resolution techniques, namely Keren and structure-adaptive normalised convolution methods. ALMD-WTR framework surpass the state-of-the-art wavelet-based algorithm, namely local motion-based video super-resolution. | en_UK |
| dc.description.coursename | PhD in Manufacturing | en_UK |
| dc.identifier.uri | http://dspace.lib.cranfield.ac.uk/handle/1826/17848 | |
| dc.language.iso | en | en_UK |
| dc.rights | © Cranfield University, 2017. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright holder. | |
| dc.subject | Resolution enhancement | en_UK |
| dc.subject | discrete wavelet transform | en_UK |
| dc.subject | interpolation | en_UK |
| dc.subject | registration | en_UK |
| dc.subject | global motion | en_UK |
| dc.subject | local motion | en_UK |
| dc.subject | satellite images | en_UK |
| dc.title | Wavelet-based image and video super-resolution reconstruction. | en_UK |
| dc.type | Thesis | en_UK |