- Title
- Iterative Refinement Versus Generative Adversarial Networks for Super-Resolution Towards Licence Plate Detection
- Creator
- Boby, Alden, Brown, Dane L, Connan, James
- Subject
- To be catalogued
- Date
- 2022
- Type
- text
- Type
- article
- Identifier
- http://hdl.handle.net/10962/463417
- Identifier
- vital:76407
- Identifier
- xlink:href="https://link.springer.com/chapter/10.1007/978-981-99-1624-5_26"
- Description
- Licence plate detection in unconstrained scenarios can be difficult because of the medium used to capture the data. Such data is not captured at very high resolution for practical reasons. Super-resolution can be used to improve the resolution of an image with fidelity beyond that of non-machine learning-based image upscaling algorithms such as bilinear or bicubic upscaling. Technological advances have introduced more than one way to perform super-resolution, with the best results coming from generative adversarial networks and iterative refinement with diffusion-based models. This paper puts the two best-performing super-resolution models against each other to see which is best for licence plate super-resolution. Quantitative results favour the generative adversarial network, while qualitative results lean towards the iterative refinement model.
- Format
- computer, online resource, application/pdf, 1 online resource (13 pages), pdf
- Publisher
- SpringerLink
- Language
- English
- Relation
- Inventive Systems and Control: Proceedings of ICISC 2023, Boby, A., Brown, D. and Connan, J., 2023. Iterative Refinement Versus Generative Adversarial Networks for Super-Resolution Towards Licence Plate Detection. In Inventive Systems and Control: Proceedings of ICISC 2023 (pp. 349-362). Singapore: Springer Nature Singapore, Inventive Systems and Control: Proceedings of ICISC 2023 p. 349 2022 2367-3389
- Rights
- Publisher
- Rights
- Use of this resource is governed by the terms and conditions of the SpringerLink Terms of Use Statement ( https://link.springer.com/termsandconditions)
- Rights
- Closed Access
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- Visitors: 34
- Downloads: 1
Collections
Brown, Dane A (Dr)
Connan, James (Mr)
RU Department of Computer Science
SDG 09. Industry, Innovation and Infrastructure
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details | SOURCE1 | Iterative Refinement Versus Generative Adversarial Networks for Super-Resolution Towards Licence Plate Detection.pdf | 700 KB | Adobe Acrobat PDF | View Details |