近年來，深度學習領域有了突破性的發展，掀起了一波波人潮相繼湧入研究此課題，所涵蓋的領域無奇不有。但就影像處理方面的應用，目前被提出來的議題，多數所設計之架構僅致力於單一任務，並藉由誤差數據與參考標準數據兩者成對的數據集訓練而成。得到之效果固然顯著，但因為需要有參考標準數據，對於日後數據集更新會有一定的難度。隨著安全意識的抬頭，電腦視覺輔助系統的需求度也日益提高，以監視攝影設備最為顯著，監視攝影設備可以紀錄事發過程，甚至是利用捕捉畫面進行辨識與偵測，有效嚇止與防範犯罪，然而若是在有霧氣造成視線不良、夜間高感光度拍攝造成影像噪化及拍攝物件高速移動造成影像模糊等原因，造成影像無法輕易用肉眼辨識，甚至導致辨識錯誤的情況發生，為解決此類問題，本文以生成式對抗網路為基礎，搭建一多功能循環生成式對抗網路架構用來執行無監督影像域轉換，其中定義了「退化」、「噪化」、「霧化」與「還原」的影像，訓練架構中的生成模組，藉由神經網路學習多組影像定義域間的轉換關係，將大多數人認為模糊、噪化及霧化的影像轉換成清楚可辨識的圖像，以達到改善無監督影像域轉換的可擴展性。

In recent years, domain translation has been a breakthrough in the field of deep learning. However, most of the issues raised so far are dedicated to a single situation, and trained through paired datasets. The effect is significant, but the defect is that the architectures lack scalability and the paired data update in the future is difficult. The demand for computer vision assistance systems is increasing, and there is more than one mission requirement in some environments. In this Thesis, we propose a multi-domain image translation model which has two advantages in terms of flexibility: one is the depth of the architecture that can be designed according to expectations, and the other is the number of domains that can be designed according to the number of tasks. We demonstrate the effectiveness of our theory on dehaze, debluring, and denoising tasks.

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