# Image Completion with Large or Edge-Missing Areas
## 4 Results  
To compare the experimental results, we tested the [PERSON] method using the same parameters as our method. The experimental results are shown in Figure 4.  
Figure 4: \(Cont.\)  
The results show that when the missing area is large (the width and length are both more than 50% in this case) or located at the border of the image, the results of the completion of the [PERSON]'s method are ambiguous. In Figure 4, the size of the input images of (1) to (3) is \(256\times 256\) pixels, wherein the missing area is an area of \(128\times 128\) size located at the center of the image, i.e., 1/4 size of the input image. It can be seen from the figures that the completion results of [PERSON]'s method will be blurred, distorted, etc., especially the difference at the boundary of the missing area is obvious. In this paper, both the global and local consistency of the completed image are considered. At the same time, the central region of the missing region in the preliminary completion result is input into the local discriminator 2 for confrontation training to guarantee the authenticity of the texture information, so the results of this paper are more clear and realistic; The input image of (4) contains a plurality of missing regions, wherein the missing regions are randomly distributed at different positions of the image. Due to the lack of context information, the completion result of the [PERSON]'s method will have obvious blank areas. In addition, when the missing area is large and located at the edge of the image, as shown in (5) to (8), the [PERSON]'s method's completion results contains pseudo-color in addition to the large-area blank. This is because the iteration speed of the discriminator in the [PERSON]'s method is quicker than the generator, which causes the instability training of generated adversarial network. We measured the running time of the discriminators and generator in the [PERSON]'s method and found that the generator running time is 50% slower than the discriminators average, which causes the discriminator to easily distinguish which one is a natural image and which one is the generated image. The gradient only makes the generator generates strange textures that produce pseudo-colors during backpropagation. In this paper, the local discriminator 2, i.e., the \"widening\" network, is added to balance the training speed of the generator and discriminators. In addition, we also replace the ReLU layer behind each convolution layer with the combination of BN layer and Leaky_ReLU layer, and remove the sigmoid activation function of the last layer of the discriminator, avoiding gradient disappearance and pattern collapse and other problems during network training, which makes the network training more stable. Experiments show that the difference between the running speed of our network and [PERSON]'s method network is only 10%. This improvement makes the network training more stable and easier to converge, and overcomes the defects of generating pseudo-color in [PERSON]'s method, making the result more  
Figure 4: The result of [PERSON]’s (**b**) method and ours (**c**). (**a**) Input. realistic. In Figures 4-7, our synthesis results are clearer and more realistic than [PERSON]'s method, but due to the lack of context information, blank areas still appear. This is a problem that we still need to improve. We use a combination of MSE and structural similarity (SSIM) to evaluate the similarity of the complemented images and the original images. Among them, the MSE is used to compare the absolute error between image pixels, i.e., the difference between pixels, and the SSIM is used to evaluate the internal dependence between image pixels, i.e., human visual perception on images. The smaller the MSE and the greater the SSIM are, the more similar the images are. Figure 5 shows the MSE result pairs and Figure 6 shows the SSIM result pairs of the [PERSON]'s method and our method on images of Figure 4. The red line denotes our method's results and the green line denotes [PERSON]'s method's results.