# SR-Inpaint: A General Deep Learning Framework for High Resolution Image Inpainting
## 5 Experimental Results and Discussion
### Qualitative Evaluation  
Firstly, our approach is compared with the state-of-the-art technology in terms of visual results. Figures 4 and 5 show the examples of the images generated by the our model and the comparison model under 2K and 4K inputs. For visualization, we replace the damaged area with black color. It is clearly visible that our model is able to generate semantically continuous results that are closer to ground-truth. In addition, most of the image structures remain coordinated. In contrast, the HiFill model does a poor job in terms of structure and semantics. Particularly, for complex background, the results of the HiFill model suffer from deformation and semantic incoherence.  
We think it is explained by the fact that the HiFill algorithm borrows the surrounding texture to fill the holes. If the structure and semantics of the missing region are completely different from the surrounding, then it is difficult to guarantee a meaningful structure. In contrast, our inpainting is based on edge connection, following the principle of \"lines first, color next\", generating coherent structures through a two-stage GAN to achieve visual realism.  
Figure 4: Example of inpainting results for 2K resolution: (**left** to **right**) ground-truth, input damaged image, inpainted image by our method, inpainted image by state-of-the-art technology.  
Figure 3: Implementation diagram of our high-resolution inpainting pipeline.  
Compared to general image inpainting, for HR inpainting, the sharpness of the inpainted area is as important as the global picture coherence. Therefore, Figure 6 shows a zoomed-in comparison of the inpainting results. It can be seen that the areas generated after bicubicly upsampling and Gaussian pyramid-up are blurred and low resolution. The areas enhanced by the wavelet method have a non-negligible color difference with the original images. Meanwhile, the areas enhanced by the Super-Resolution (SR) enhancement mechanism show minimized ambiguity. Compared with the traditional techniques, the SR enhancement mechanism based on deep learning achieves the reconstruction of high frequency details at high resolution, which significantly improves the sharpness of the generated image.  
Although the HiFill model can also generate high frequency details at high resolution through the Contextual Residual Aggregation (CRA) mechanism. The CRA mechanism aggregates high-frequency details using background residuals. However, if the high-frequency information in the background is not relevant to the high-frequency information in the missing region, then the generated high-frequency details are meaningless. Compared with the CRA mechanism, SR enhancement mechanism generates high-frequency details through global picture inference based on deep learning of big-data, which is guaranteed to be meaningful in most cases.  
Figure 5: Example of inpainting results for 4K resolution: (**left** to **right**) ground-truth, input damaged image, inpainted image by our method, inpainted image by state-of-the-art technology.  
In summary, although both generate HR results, our method does significantly better than the current state-of-the-art HR inpainting method in terms of semantic consistency as well as structural continuity. Our strategy maximizes the visual realism of the inpainting.