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Single image super-resolution reconstruction

Resource type
Thesis type
(Thesis) M.A.Sc.
Date created
2022-12-14
Authors/Contributors
Abstract
Several deep learning-based models for single image super-resolution (SISR) have been presented in recent years that achieve superior performance compared to the traditional vision-based approaches. However, the performance of each \ac{SR} model varies for different image content. For example, one model may outperform the others on an image with high and intricate textures, while another may perform better in reconstructing images of structured scenes. To utilize the best capability of each model for SR reconstruction, we propose a system that uses the image content to select the most suitable model for SR reconstruction by taking advantage of each image's dominant content. The proposed system is tested on the standard benchmark datasets for SISR. The obtained results indicate that the proposed system delivers comparable results with the state-of-the-art methods in terms of the overall quantitative metrics and visual quality, and confirmed that it delivers better results for scene specific content.
Document
Extent
77 pages.
Identifier
etd22335
Copyright statement
Copyright is held by the author(s).
Permissions
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Supervisor or Senior Supervisor
Thesis advisor: Saeedi, Parvaneh
Language
English
Member of collection
Download file Size
etd22335.pdf 46.97 MB

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