Enhancing Image Resolution with Generative Adversarial Networks
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Date
2022
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Journal ISSN
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Publisher
Institute of Electrical and Electronics Engineers Inc.
Abstract
Super-resolution is the process of generating high-resolution images from low-resolution images. There are a variety of practical applications used in real-world problems such as high-definition content creation, surveillance imaging, gaming, and medical imaging. Super-resolution has been the subject of many researches over the past few decades, as improving image resolution offers many advantages. Going beyond the previously presented methods, Generative Adversarial Networks offers a very promising solution. In this work, we will use the Generative Adversarial Networks-based approach to obtain 4x resolution images that are perceptually better than previous solutions. Our extensive experiments, including perceptual comparison, Peak Signal-to-Noise Ratio, and classification success metrics, show that our approach is quite promising for image super-resolution. © 2022 IEEE.
Description
Keywords
Deep Learning, Generative Adversarial Networks, Image Processing, Machine Learning, Super Resolution
Turkish CoHE Thesis Center URL
Citation
1
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Source
Proceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022 -- 7th International Conference on Computer Science and Engineering, UBMK 2022 -- 14 September 2022 through 16 September 2022 -- Diyarbakir -- 183844
Volume
Issue
Start Page
104
End Page
109