隨著深度學習的快速發展，通過一個個深度模型，越來越多的視覺問題得以解決。對於視頻解模糊這個任務，已經有一些深度模型被提出。與此同時，生成對抗網路也由於它強大的能力而被廣泛應用到許多場景。在這篇論文中，我們將傳統方法中經典的多尺度結構與生成對抗網路相結合以來解決視頻解模糊問題。從試驗結果中可以發現，無論是從數值度量上還是視覺直觀上，我們的模型都比一些其他的深度模型算法更加穩定。最後，我們也從不同的角度分析了我們的模型的優越性。

With the rapid development of deep learning, more and more vision problems can be solved by using deep neural network models. Several deep models have been released to handle video deblurring task. At the same time, generative adversarial networks (GANs) are also widely used in many kinds of problems for its strength. In this thesis, we connect the classical multi-scale structure in traditional vision methods with GAN for video deblurring. The quantitative and qualitative results of our experiments demonstrate the proposed model can restore frames more robustly than the state-of-the-art video deblurring deep methods. We also justify the superiority of our model from different perspectives.

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