# Parallel Image Completion with Edge and Color Map
## 4 Experimental Results and Analysis
### Qualitative Comparison  
Figure 4 shows a sample of images completed by PIC-EC. For visualization, the colors of the composite edge maps \(E_{comp}\) are reversed, and the generated parts are dyed blue. The PIC-EC is able to keep the image structure and color map intact with a large fraction of image damaged. Furthermore, the completed images exhibit realistic results and minimal blurriness because the edge and color generators can learn enough information to guide the image completion network. We also compare the qualitative performance of PIC-EC with the existing image completion methods using the images with irregular masks as well as the squared ones. The implementations of all these methods are based on their released source codes and pre-trained models. Note that no post-processing steps are performed for all these methods to ensure fairness of the comparisons. Figures 5 and 6 summarize the qualitative comparison results on the CelebA-HQ dataset with irregular masks and squared masks, respectively. Figure 7 shows the images generated by PIC-EC with those generated by other methods on the Paris StreetView dataset with regular masks. From the results, the traditional method FMM (Fast Marching Method) [31] has no ability to generate semantic contents, especially in the larger squared mask cases, as shown in Figures 6c and 7c. It is not surprising because FMM starts from the boundary of  
Figure 3: Irregular mask examples of the QD-IMD: Quick Draw Irregular Mask Dataset. the missing region and goes inside the region gradually filling everything in the boundary using the Fast Marching Method. It takes a small neighborhood around the pixel on the neighborhood to be completed. This pixel is replaced by a normalized weighted sum of all the known pixels in this neighborhood. It does not consider the semantic relevance and is only suitable for completing the small missing regions. In Figures 5d and 6d, GLCIC (Globally and Locally Consistent Image Completion) [19] shows obvious visual artifacts including blurred and distorted images in the masked regions, and it struggles to generate consistent structures with the remaining image context because of insufficient understanding of the image characteristic and ineffectiveness of convolutional neural networks in modeling long-term correlations between contextual information and the missing region. As shown in Figures 5f and 6e,g, the images completed by DeepFill [36] and GMCNN [37] have severe color distortion. Therefore, these methods can not accurately fit the color distribution over CelebA-HQ dataset. Because DeepFill adopts a patch matching method in its contextual attention module, it fails to achieve plausible results even through the refinement network. In Figure 7d, GMCNN generates the distorted structures in the doors and windows. The deconvolution operations in GMCNN lead to the structure and color distortion. Although DFNet [38] shows a fine performance, it shows a lack of relevance between the hole and background regions such as symmetry of eyes, as shown in Figures 5e and 6f. The images completed by the proposed method PIC-EC and the state-of-the-art method EdgeConnect [23] are closer to the ground truth than images from other methods. Both of them are able to recover correct global structures. However, the PIC-EC method can generate sharper details than EdgeConnect such as the eyes and mouth, as shown in Figures 5g,h, 6h,i and 7e,f. Due to EdgeConnect losing image color information when recovering the global structures, it may fill in incorrect details in some missing areas. When edge and color information is present simultaneously as the guidance, the image completion network of PIC-EC only needs to learn the fine details without having to worry about preserving image structure and color maps. Therefore, the images generated by PIC-EC demonstrate the superiority in preserving both appearance and structure coherence. Figure 4: The images produced by PIC-EC.