請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88347
標題: | 應用生成對抗網絡與用圖像品質評估的變換器 Applying Generative Adversarial Networks with Transformer for Image Quality Assessment |
作者: | 羅費南 Fernando Sebastian Lopez Ochoa |
指導教授: | 廖世偉 Shih-wei Liao |
關鍵字: | 圖像增強,生成對抗網路,低光照圖像增強,圖像品質評估,變換器, Image Enhancement,Generative Adversarial Networks,Low-Light Image Enhancement,Image Quality Assessment,Transformers, |
出版年 : | 2023 |
學位: | 碩士 |
摘要: | none Image Quality Enhancement is the process in which images can be improved for better human interpretation of its contents. Image enhancement is usually done based on certain parameters specified when formulating the problem. Generative Adversarial Networks (GAN), on the other hand, can create new images based only on characteristics it finds on the training set, without specifying those characteristics. We utilize three variations of the GAN architecture, Cycle GAN, Conditional GAN and EnlightenGAN, to implement different solutions to generate images with an increased image quality on existing datasets. Our goal is to demonstrate that Transformer for Image Quality Assessment, an image quality evaluation framework, can give a frame of reference for the performance of these GANs, and that those GANs can increase the quality of images after being trained on an original and enhanced group. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88347 |
DOI: | 10.6342/NTU202210041 |
全文授權: | 同意授權(全球公開) |
顯示於系所單位: | 資訊工程學系 |
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ntu-111-2.pdf | 60.82 MB | Adobe PDF | 檢視/開啟 |
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