# Image Completion with Large or Edge-Missing Areas
## 2 Related Work
In view of the shortcomings of the above methods, [PERSON] et al. (2018) proposed using the encoder-decoder convolutional neural network as the global content constraint and the similarity between the missing regions and the original regions as the texture constraint, the two combined to perform image completion. [PERSON] divided the high-resolution image into several steps to enhance the authenticity of the texture information, but it was difficult to ensure the global consistency of the complemented images. To make sure the consistency of the complemented images, [PERSON] uses a global and local consistency method to complete the images ([PERSON] et al., 2018). First, the method generates missing regions and the corresponding binary channel mask (1 indicates the region to be completed and 0 indicates the intact region) on the original image randomly. Second, the images with missing regions and corresponding binary channel masks are input into the completion network with the mean square error loss (MSE Loss) of themissing regions in the original image and the complementary regions in the generated image to train the completion network. Third, the generated image and the region centered on the complemented region are input into the global discriminator and the local discriminator respectively while keeping the completion network fixed. The two networks are trained by using the generated adversarial loss. At last, the three networks are trained as a whole with the combination of the three losses. [PERSON]'s method is better than the existing image completion methods, but there are still problems: (1) when the missing areas are large, the final completion results are blurry; (2) when the missing areas are located at the border of the images, the lack of context information and the inconsistency training speed between the completion network and the discriminator network leads to the problem of blurring and color distortion. In addition, the networks structure adopted in [PERSON]'s method is the basic GAN model. One of the most obvious shortcomings of the basic GAN is that the training process is difficult to converge and has great instability. The reasons are that (1) different from the general training process by the gradient descent method, which has a clear objective function, GAN's training is a process of finding Nash equilibrium points. The decrease in the error of either side of the generator and the discriminator during the training process may causes the error of the other party to rise. It is difficult for GAN to reach a relatively balanced state, and always oscillate between various modes of generated samples, one of the most common case is that GAN maps different input samples to the same generated sample, i.e., repeatedly generates the same data. This phenomenon is also called model collapse. (2) In general, since the iterative speed of the discriminator is quicker than that of the generator, the inconsistent iteration speed of the two will leads to the instability of the GAN model, therefor it is difficult to obtain an optimal model. (3) It is prone to the phenomenon that the gradient disappears during the backpropagation process, which makes the model more difficult to train. In response to the shortcomings of GAN, the researchers derived a series of new models based on the basic GAN model, such as Conditional GAN (cGAN) [33], Deep Convolutional GAN (DCGAN).) [34], Wasserstein GAN (wGAN) [35] and so on. Among them, wGAN is mainly to improve the stability of the basic GAN's training. The generation problem can be approximated as a regression problem; however, the last layer of the discriminator in the basic GAN's network structure uses the sigmoid activation function, which approximates the generation problem to the 0-1 two classification problem that causes problems, such as mode collapse and difficulty in convergence, and difficulty for the generator and the discriminator to reach an equilibrium.