# Image Structure-Induced Semantic Pyramid Network for Inpainting
## 3 Experiment and Analysis
### Qualitative Comparisons  
To evaluate the accuracy of the proposed method for the prediction of missing image edges, U-Net and the residual U-Net used by the mentioned method are compared on the CelebA-HQ dataset, and the experimental results are displayed in Figure 4. By comparison, the image edge prediction graph obtained by the residual U-Net network in this article is basically the same as the original image edge structure graph obtained by the Canny operator, and the edge prediction effect is good.  
Figure 4: Method in this paper is compared with the result of the Canny operator. (**a**) Ground truth. (**b**) Damaged greyscale. (**c**) Canny operator edge diagram. (**d**) U-Net. (**e**) Our method.  
The inpainting outcome of the proposed method on the CelebA-HQ datasets are represented in Figure 5. It can be shown from the figure that the inpainted image is basically the same as the real image in colour, form and style, and the texture particulars of the image are more accurate and vivid. The boundary between the missing area and the background region is naturally clear, with consistent visual effects and no mark of inpainting. This suggests that the proposed method has a big impact on the image inpainting task.  
To objectively evaluate the inpainting results of the mentioned method, this method is compared with CE [6], PEPSI [31], EC [9], PEN-Net [20], and CTSDG [32]. Figures 6-8 show the comparison results of the proposed method and the above four methods on the Places2, CelebA-HQ and Facade datasets, where the masks are divided into regular masks and irregular masks.  
In contrast, the Context encoder method often generates artefacts. The PEPSI method can generate results with relatively continuous textures, but its generated results often have structural errors. The EdgeConnect method has a reasonable semantic structure for the sparse damaged image, but it cannot repair the damaged image at a reasonable pixel level. The PEN-Net method can generate restoration results with clear texture for large-area damaged images, but it is prone to the problem of blurred edges. The CTSDG algorithm is richer in inpainting details than other algorithms, but some areas do not recover the proper structure and there are obvious inpainting traces. The inpainting effect of the proposed method for the Places2 dataset with damaged areas is better than that of other methods. The generated inpainting results not only have reasonable structure but also have clear texture details, and the inpainting effect is clearly superior to other comparative methods.  
Figure 5: Method result graph of this paper. (**a**) Ground truth. (**b**) Damaged figure. (**c**) Edge of the figure. (**d**) Our method.  
Figure 6: Comparison of the inpainting effects of different methods on the Places2 dataset. (**a**) Input. (**b**) CE. (**c**) PEPSI. (**d**) EC. (**e**) PEN-Net. (**f**) CTSDG. (**g**) Our method. (**h**) GT.  
Figure 7: Comparison of the inpainting effects of different methods on the CelebA-HQ dataset. (**a**) Input. (**b**) CE. (**c**) PEPSI. (**d**) EC. (**e**) PEN-Net. (**f**) CTSDG. (**g**) Our method. (**h**) GT.