# Image Completion with Large or Edge-Missing Areas
## 4 Results
As can be seen from Figure 5, when the missing area are large, the MSE results of [PERSON]'s method are higher than our method, which indicate that our method guarantees the realistic of texture information when completing large missing areas. As shown in 4 to 8 of the horizontal coordinate in both Figures 5 and 6, when the missing area are located at the border of the image, both difference of MSE and SSIM between the results of [PERSON]'s method and our method with the original images have significant difference, which indicate that the method of this paper has better completion results than [PERSON]'s method in context information and global and local consistency, and the completed images are closer to the original images. Figure 7 shows the operation of each loss in the training process of the network structure using the ReLU layer and the sigmoid activation function in the last layer of the discriminator in [PERSON]'s method. Figure 8 shows the operation of each loss in the training process of the network structure  
Figure 5: The MSE result pairs of our method and [PERSON]’s method on images of Figure 4.  
Figure 6: The SSIM result pairs of our method and [PERSON]’s method on images of Figure 4.  
using the combination of BN layer and leak_relu layer which replaces the ReLU layer, and the sigmoid activation function of the last layer is removed, also, the local discriminator 2 is added. It can be seen from the figures that the gradient disappearance phenomenon is easy to occur during the training of the network structure of [PERSON]'s method, and the training process is very  
Figure 8: The losses graph of this method’s network structure. Figure 7: The losses graph of the [PERSON]’s network structure. unstable. The training process of the network structure adopted in this method is relatively stable, and when the generation loss decreases, the adversarial loss shows an upward trend, and the two always maintain the confrontation state until the balance is reached. The following is another set of experimental results. We used the same parameter settings as the grass to train and complete the image of the barbed wire with certain structural features. The result of the completion is shown in Figure 4:0  
In addition, to verify the generalization ability of the proposed method, we randomly extracted 15,000 images from the Places2 dataset for training. The test results are shown in Figure 4:1  
Figure 4:2 _Cont_. Figure 4:3 The results comparison of [PERSON]’s method and ours with barbed wire images. It can be seen from the figures that the complemented area of the image generated by [PERSON]'s method are very blurred, and the boundary areas are more obvious, which cannot maintain the authenticity and consistency of the image well. Our method has improved the network structure, so that the network avoids the gradient disappearing during the training process, and reduces the inconsistency of the generator and discriminator iteration speed, which prove that the method is able to generate clearer, more realistic images and guarantee the globally and locally consistency of the images.