# SR-Inpaint: A General Deep Learning Framework for High Resolution Image Inpainting
## 1 Introduction  
Image inpainting or image completion, which involves the automatic recovery of missing pixels of an image according to the known information within the image, is an important research area in computer vision. With the rapid development of digital image editing technology, image inpainting has been widely applied to damaged photo restoration, occlusion removal, intelligent aesthetics and other graphics fields. Inpainting has been an active research area in the past few decades and many studies have been devoted to achieving visual realism and vividness [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]. However, due to the complexity of damaged images and the inherent ambiguity of methods, the semantics-continuous and texture-clear inpainting remains a major challenge, especially for High-Resolution (HR) images [17]. Hence, our work is motivated by the issue that most existing image inpainting techniques cannot realize high quality completion of damaged HR images. Early inpainting methods can be broadly divided into the diffusion methods based on pixel propagation [1, 2, 3] and the patching methods based on texture borrowing [4, 5, 6, 7, 8], which do a poor job of reconstructing complex details [9]. In recent years, deep learningapproaches have achieved promising success in inpainting. A stream of these methods hallucinates missing pixels using learned data distribution [9; 10; 11; 18]. Another stream fills the hole using a data-driven manner with the external image sources [12; 13; 14; 15; 16]. Though these methods can yield meaningful structure in missing regions, the generated regions are often blurred and accompanied by artifacts. In addition, with the improvement of Internet transmission capacity and mobile device cameras, the resolution of image and video sources available to users via the cloud or locally is increasing [17]. However, for HR images, general image inpainting methods often yield a limited result. In addition the input is even rejected due to the memory limitation [17]. Now, there is an urgent need for methods that can reconstruct the missing high-frequency information in HR images and generate sharp texture details. Therefore, several inpainting strategies have been proposed for the high-resolution reconstruction of high-frequency information. For example, [PERSON] et al. [19] proposed a combined framework of patch-based inpainting and super-resolution to generate a dense high-resolution depth map from a corrupted low-resolution depth map and its corresponding high-resolution texture image. [PERSON] et al. [20] proposed a method called \"Zoom-to-Inpaint\", which enhances the high-frequency details of the inpainted area through a zoom-in, refine and zoom-out strategy, combines with high-resolution supervision and progressive learning. These frameworks improve the high-frequency reconstruction of the missing regions in general images. However, for HR images, these methods are not yet perfectly applicable and still face problems such as computational limitation. On the other hand, [PERSON] et al. [17] proposed an HR image inpainting algorithm, which upsamples the Low-Resolution (LR) inpainted result and adds a high-frequency residual image into the blurred image to generate a sharp result through a contextual residual aggregation mechanism. The method effectively suppresses the cost of memory and computing power as well as achieves compelling quality in natural photographs with a monotonic background. However, the realism and semantic continuity of the inpainted results for the images with complex compositions or textures need to be further improved. For now, the visually realistic recovery of high-frequency information for the HR images with complex backgrounds is still a tricky task. To this end, we propose a novel deep learning framework for HR image inpainting.