# Image Completion with Large or Edge-Missing Areas
## 2 Related Work  
At present, many methods have been introduced to solve the problems of image completion. Image completion based on diffusion is the first digital repair method. In this method, missing pixels are filled by diffusing image information from a known region to a missing region at the pixel level. These algorithms are based on variational methods and the partial differential equation (PDE) theory. The PDE algorithm was proposed by [PERSON] et al. (2018). The algorithm is an iterative algorithm, and its primary goal is to propagate the gradient direction and the grey value of the image to the interior of the region to be filled and to solve the demand for high-order partial differential equations in image processing. If the missing areas are small, the PDE algorithm will produce good results. However, when the missing areas are large, the algorithm will take a long time to process and will produce bad results. Vague areas will be produced by the algorithm that make the filling area unnatural. Another more traditional approach is to fill a missing image area with a large external database in a data-driven manner. These methods assume that areas surrounded by similar environments may have similar content ([PERSON] et al., 2018; [PERSON] et al., 2019; [PERSON] et al., 2019), such as [PERSON] et al. assume that the input image is taken at a well-known landmark, so similar images taken at the same location can be easily found on the Internet. This method is very effective in finding sample images with enough visual similarity to the query but may fail when the query images are not of good quality in the database. These methods also require access to an external database, which greatly limits the possible application scenarios. Compared with data-driven technology, the sample-based method can perform more complex image filling task and fill large areas of missing regions in natural images. Sample-based image completion method was initially used for texture synthesis by resampling either pixels ([PERSON] et al., 2018) or whole patches ([PERSON] et al., 2018) of the original texture to generate a new texture. This method is then extended to image mosaic ([PERSON] et al., 2018) by image segmentation and texture generation based on energy optimization. In image completion applications, this method has been improved ([PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018), such as the best patch search method. In particular, [PERSON] et al. (2018) and [PERSON] et al. (2018) proposed a method based on global optimization to obtain a more consistent filling. These techniques were later accelerated by a random block search algorithm (PatchMatch) ([PERSON] et al., 2018), allowing real-time advanced image editing. However, sample-based completion method works well only when the missing region is composed of simple structure and texture. In recent years, deep learning has also shown outstanding results in image completion. It can fill large missing areas while preserving semantic and contextual details and capturing the high-level features of images. Recently, deep neural networks have been used in texture synthesis and image stylization ([PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018). In particular, [PERSON] et al. (2018) trained an encoder-decoder convolutional neural network (context encoder) with L2 loss and adversarial loss ([PERSON] et al., 2018) to directly predict lost image regions. This work predicts a reasonable image structure and requires only one forward propagation, which is very fast. Although the results are encouraging, sometimes it lacks fine texture details and creates visible artifacts around the boundaries of the missing areas. In addition, this method is also not suitable for processing high-resolution images. The adversarial network needs to adjust the network parameters inversely according to the loss between the generated image and the real image, so it is difficult to train against the loss when the input is large.