## 1 Introduction  
Although videos are widely used in social life, they remain incomplete and indistinct in many situations. Therefore, video restoration is significant in these cases. However, no existing models can perform both video inpainting (also known as video completion or video hole filling) and super resolution (SR) synchronously. In this paper, we aim to reconstruct the missing region and increase the resolution in frames with a generalized model, leading to coherent and high-resolution results spatially in each frame and temporally along different frames. In the situation of inpainting, pixels in the video frame may be corrupted during transmission, or some undesirable situations, such as watermarks or objects, need to be removed. Image inpainting has been extensively studied in the literature, which is consistent with video inpainting tasks. Texture synthesis methods in early works [1, 2] match and copy background patches to fill the hole. However, they cannot handle the challenging cases where inpainting regions are complex and non-stationary. The major obstacle is that these methods cannot learn to understand the semantics of images and rely heavily on matching low-level, human-crafted features. Recently, deep generative inpainting methods based on convolutional neural networks (CNN) [3] and generative adversarial networks (GANs) [4; 5] have demonstrated great improvements over traditional methods. These methods learn to understand the semantics of images by training on large datasets and perform much better on non-stationary cases, such as incomplete objects, faces, complex scenes and many others. However, these methods for image inpainting have inferior performance when applied to the task of video inpainting, where the missing content in frames is required to be spatially coherent (in each frame) and temporally consistent (across frames). Although some work has attempted to extend image inpainting methods to videos, their methods perform poorly when both the background and holes are not static [6]. As a result, it is essential to combine spatio-temporal information not only from the current frame but also from the nearby frames. The existing video inpainting algorithms can be divided into object-based and patch-based methods. In the object-based method, a video is divided into its background and foreground, which are inpainted separately and merged at the end of the algorithm [7; 8]. However, these algorithms are not robust and produce artifacts when objects are in motion. On the other hand, patch-based methods search for suitable patches from global and local frames based on both 2D patches [9] and 3D patches [10; 11; 12]. Although patch-based methods have some successful cases, they still have some drawbacks, such as heavy computation and unstable performance in videos with motion and occlusion. In the situation of super-resolution reconstruction, a non-linear mapping is used to reconstruct high-resolution (HR) images from low-resolution (LR) images. Although several methods have achieved good performance, they still remain intractable for the following ill-posed problem: how to model the mapping relationship from LR to HR images. Especially, an LR image with identical representation can be obtained from an infinite number of HR images by downscaling, resulting in a large space of possible functions during mapping. It is particularly difficult to learn an appropriate function in such a large space, which ultimately limits the capability of the model. Although several recent models have significantly improved SR performance, such as SRGAN [13], SRNTT [14], and RDN [15], they still suffer from the fact that the space of the possible mapping functions remains large. Furthermore, the direct application of the above methods to video super resolution is not appropriate due to the particularities of videos. Previous research [16; 17; 18] on video SR still represents a simple extension of image super-resolution methods.