Exploring Transformers and Degradation Methods in the Super Resolution Field

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Journal Title
Journal ISSN
Volume Title
Perustieteiden korkeakoulu | Master's thesis
Date
2022-12-12
Department
Major/Subject
Machine Learning, Data Science and Artificial Intelligence
Mcode
SCI3044
Degree programme
Master's Programme in Computer, Communication and Information Sciences
Language
en
Pages
63+32
Series
Abstract
Super Resolution is one of the most difficult fields to explore as the real world degradations are unknown and hard to be mathematically modeled. This research project aims at exploring different approaches for improving both efficiency and results of the existing algorithms by adapting a denoising method for the Super Resolution task and implementing a new degradation pipeline which would better simulate the real scenarios. The method was evaluated on three datasets containing reference images and performs the best on average. For real images which do not contain a reference, our solution provides results with more details and textures, therefore having a more pleasant looking outcome.
Description
Supervisor
Jung, Alexander
Thesis advisor
Yu, Tian
Yamac, Mehmet
Keywords
Super resolution, transformers, degradations, encoder-decoder
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Citation