# Image Completion with Large or Edge-Missing Areas
## 1 Introduction  
Image completion technology is designed to synthesize the missing or damaged areas of an image and is a fundamental problem in low-level vision. The technology has attracted widespread interest in the field of computer vision and graphics because it can be used to complement occluded image areas or to repair damaged photographs [1; 2]. In addition, it can erase distracting scene elements or adjust the object position in the image for better composition, or restore the image content in the occluded image area [3; 4; 5]. These and many other editing operations require automatic completion of missing areas, but due to the inherent ambiguity of the problem and the complexity of natural images, it is still a challenging task to synthesize content with reasonable detail for any natural image. At present, although many methods for image completion have been proposed, such as sample-based [6; 7; 8; 9] and data-driven [4; 10; 11] image completion, problems remain. Not only does the texture pattern need to be completed, but the structure of the scene and the object to be completed must be understood. In the past, the main method of image completion was to copy the existing image block in the uncorrupted area to the missing area. However, this method can achieve effective results only when the image to be complemented has a strong structure, the texture information such as the color of each region has strong similarity, and the missing region has a regular shape [12; 13; 14; 15; 16]. This approach has significant limitations for image completion work with complex structures and contents. Therefore, the method of intercepting an image block directly from surrounding information is not versatile. Deep learning has advanced substantially and been used in image completion due to the strong learning ability of deep neural networks. The usual deep learning-based image completion methods are completed by the generation adversarial network (GAN) [17]. The generator and discriminator in GAN can generate clear and reasonable texture content [18; 19; 20]. However, the existing GAN-based image completion methods have important limitations. GAN adopts an encoder-decoder structure inwhich two fully connected networks (FCNs) are used as intermediate layers connecting two structures. However, the existing image completion methods using GAN cannot maintain the consistency of the image space structure and context information. Therefore, most of the methods proposed above can only synthesize content with a small missing area, and the synthesis method depends on the low-level features of the image. Additionally, the input can only be a fixed-size picture. When the corrupted image has a large missing area, a complicated structure, or is located at the border of the image, the result is not ideal. To solve the above problems, [PERSON] et al. [21] proposed a globally and locally consistent image completion method. However, when the areas of the missing regions are large, it will generate distortion and blurred results. Notably, when the missing areas are located at the edge of the image, the results will have blanking and color distortion due to the lack of surrounding information and the instability of training (Figure 1). To solve the problems of completing large missing areas or regions located at the border of the image, and overcome the problem of unstable training of adversarial network, this paper proposes an image completion method with large or edge-missing areas, and makes improvements of the network structure used in [PERSON]'s method. The contributions can be summarized as follows:  
* First, by separating the middle region of the preliminary complemented area and inputting it into the local discriminator for adversarial training overcomes the problems of the existing method, such as ambiguity and distortion when the missing areas are large. It not only ensures the authenticity and clarity of the content of the central area, but also guarantees the local consistency between the central area and surrounding area.