# Parallel Image Completion with Edge and Color Map
## 5 Discussion  
Image completion is a challenging task because it is an ill-posed inverse problem and requires generating visually pleasing new pixels that are semantically consistent with the surrounding regions. Therefore, prior knowledge plays a vital role in this task to achieve meaningful and visually believable results. Under the guidance of this idea, we use both edge and color information as the prior knowledge for image completion. The results presented in the previous section have suggested that our method PIC-EC achieved superior performance on standard benchmarks, both qualitatively and quantitatively.  
Figure 4 showed that \(G_{1}\) and \(G_{2}\) are capable of hallucinating edge and color maps in the missing regions given the rest of the image. It is essential for the following image completion task. Edge and color maps recovery is a relatively easy task compared to image completion. Thus, the image completion network only needs to synthesize the details for the missing region and this will greatly reduce the pressure on the image completion network. As illustrated in Figure 5, the qualitative comparison results over CelebA-HQ with irregular masks, our PIC-EC method performed better than other methods in terms of image structural integrity, color accuracy and texture details such as the eyes and mouth. Figures 6 and 7 led to a similar conclusion. Tables 3 and 4 and Figures 8 and 9 quantitatively demonstrated comparison results for six different masks. These results suggested that, as the damaged area of the image increases, the performance of all the methods decreases. In general, this is a normal phenomenon. However, PIC-EC is minimally affected because edge and color maps can provide enough prior knowledge for the image completion network. The user study further indicated that the images synthesized by PIC-EC were closer to the ground truth images under human visual perception.  
In the domain of image-to-image (I2I) translation [26; 27; 39], photo-realistic images can be synthesized from sparse binary edge maps by cGANs. Although the images generated by these methods were not so accurate, they suggested that the edge map is an effective prior for image generation. Existing research [52] recognizes the critical role played by the color map which can be seen as an explicit style feature in the domain of image reconstruction. It leads us into considering the introduction of the color map to  
Figure 11: Failure cases of PIC-EC. (**a**) ground truth images; (**b**) masked images; (**c**) generated edge maps; (**d**) generated color maps; (**e**) completion results.  
improve the style consistency between the missing region and the remaining image context. Our study aims to contribute to this growing area of research by exploring the role of prior knowledge in the image completion task.  
Although PIC-EC has achieved semantic plausibility and visually pleasing results, there are also some limitations existing. Firstly, PIC-EC is easily fooled by the adversarial examples that will degrade the quality of features and affect the performance of PIC-EC. Secondly, since edge and color generators are fundamental to the PIC-EC, once they are not working properly, PIC-EC fails to accurately complete the missing region. This happens sometimes in richly textured areas, or when a large portion of the side face image is missing. For this reason, improving the robustness against the adversarial examples and the performance of edge and color generators have become the important research directions in the future.