影像超解析技術的主要目的是利用低解析度影像產生高解析度影像，並盡可能的保留影像資訊、維持自然的紋理樣貌。在本論文中提出了一個基於循環生成對抗網路的影像超解析演算法。此方法中有兩組生成對抗網路，其中一組著重在由低解析度影像生成高解析度影像，另一組則是用來增加資料多樣性，以強化整體網路的效能。經過訓練之後，得到一個低解析度影像與高解析度影像的非線性映射關係。
實驗結果顯示，本論文提出的影像超解析演算法，相較於其他方法，在客觀影像評估標準以及主觀的影像品質上皆有較好的表現。

The main purpose of super-resolution technology is to generate high-resolution (HR) images from low-resolution (LR) images, and preserve image information as much as possible to maintain natural texture.
In this Thesis, a super-resolution algorithm based on the cycle generative adversarial network is proposed. There are two generative adversarial networks, one of which focuses on generating high-resolution images from low-resolution images, and another is used to increase data diversity to enhance performance. A nonlinear mapping relationship between low-resolution images and high-resolution images is calculated after training.
The experimental results show that the proposed approach has better performance than other methods on subjective visual quality and objective measurement

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