Please use this identifier to cite or link to this item:
https://www.um.edu.mt/library/oar/handle/123456789/35413
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.date.accessioned | 2018-10-30T11:26:09Z | - |
dc.date.available | 2018-10-30T11:26:09Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Mifsud, R. (2018). Super-resolution of license plates (Bachelor's dissertation). | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar//handle/123456789/35413 | - |
dc.description | B.SC.(HONS)COMP.SCI. | en_GB |
dc.description.abstract | Nowadays, image enhancement plays an essential role in crime scene investigation and the monitoring of offences on public roads. CCTV videos usually contain several distortions, including poor illumination, low-resolution, optical distortion and compression artefacts. As a consequence, specific important details may be blurred, hindering recognition of license plates. Super-resolution can be mainly divided into two categories: Single-image super-resolution and Multiple-image super-resolution. Currently, multiple-image super-resolution is the main technique which is being used to improve image quality in forensic science. However, this approach has various limitations. In this dissertation, single-image super-resolution techniques were adapted to restore license plates. This project explores the use of class-based super-resolution techniques which were successfully implemented in face and iris super-resolution. This work proposes three different approaches to class-based single-image super-resolution. The first methodology which was used is the simple least-squares method. This technique consisted of posing the super-resolution problem as a least-squares problem to minimise the reconstruction error of the images and achieve appropriate reconstruction weights. The second approach was super-resolution by Eigen transformation. This method utilises principal component analysis (PCA) to perform the super-resolution procedure while significantly reducing the dimensionality of the dictionaries which were used. Finally, a neighbour embedding method was implemented. This method uses a patch-based approach, which divides the images into a number of overlapping patches and performs the super-resolution procedure on every patch in order to obtain the best reconstruction weights for each patch. Experiments show that the first two methods presented overfitting which significantly hinders the character recognition process due to the global nature of these approaches. Thus, they are not able to adapt to different license plate structures. On the other hand, the neighbour embedding method yielded satisfactory results. This method was consistently shown to surpass the perceived quality of license plates processed through bilinear interpolation. | en_GB |
dc.language.iso | en | en_GB |
dc.rights | info:eu-repo/semantics/restrictedAccess | en_GB |
dc.subject | High resolution imaging | en_GB |
dc.subject | Automobile license plates | en_GB |
dc.subject | Interpolation | en_GB |
dc.title | Super-resolution of license plates | en_GB |
dc.type | bachelorThesis | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
dc.publisher.institution | University of Malta | en_GB |
dc.publisher.department | Faculty of Information and Communication Technology. Department of Computer Science | en_GB |
dc.description.reviewed | N/A | en_GB |
dc.contributor.creator | Mifsud, Ryan | - |
Appears in Collections: | Dissertations - FacICT - 2018 Dissertations - FacICTCS - 2018 |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
18BCS010.pdf Restricted Access | 1.49 MB | Adobe PDF | View/Open Request a copy |
Items in OAR@UM are protected by copyright, with all rights reserved, unless otherwise indicated.