# Chinese Character Image Completion Using a Generative Latent Variable Model
## 3 Results
### Image Restoration Performance Comparison  
We compare the VAE-C model with image inpainting technologies. Image inpainting technologies are mainly used in a model for restoring corrupted images [2; 3; 4]. We randomly create a corrupted image using the mask algorithm provided by the partial convolution model (PConv) [3] and restored the corrupted image with each model. All of these experiments were conducted in the same environment, and the learning time was 3 s per 10 epoch based on 1 image for Graphics Processing Unit (GPU); the results of all models were the same. When outputting the result images, the VAE-C models may take more time than the other two models because they have a process to control the target node value.  
Figure 11 shows the qualitative results of the restoration of the corrupted image that arbitrarily damaged the Chinese character images using each model. The image inpainting (PConv) model [3] and the gated convolution (GConv) model [2] show that some areas have not been restored or are over-injected compared to the VAE-C model, which is different from the original image. This result shows that it is not appropriate to use the image inpainting technologies PConv model and the GConv model for the restoration of corrupted Chinese character images. At this time, the GConv model was used without setting the user-guided option.  
To further compare the corrupted image restoration performance of the two models--image inpainting and VAE-C--the same experiment was conducted with the Places2 dataset [39], celebrity faces attributes (CelebA) [40] dataset, and Canadian Institute for  
Figure 11: The results of restoring a corrupted image (**a**) that has corrupted the target image (**e**) using the partial convolution model (PConv) (**b**) [3], gated convolution model (GConv) (**c**) [4], and VAE-C model (**d**).  
Advanced Research (Cifar-10) dataset. The Places2 and CelebA datasets are frequently used to compare image restoration performance. The Places2 dataset, a repository of 10 million scene photographs labeled with scene semantic categories, comprises a large and diverse list of the types of environments encountered in the world [39]. CelebA is a large-scale face attributes dataset with more than 200,000 celebrity images, each with 40 attribute annotations [40]. Cifar-10 is a multi-class dataset consisting of 60,000 32 \(\times\) 32 color images in 10 classes [41]. A Cifar-10 dataset with a relatively low-resolution image will serve to show what results will be obtained when the model is tested with a low-resolution image. Figure 12 shows the qualitative results of this experiment. PSNR, SSIM, and MSE scales were used to determine whether the VAE-C model was the best model to restore a corrupted image. Figures compared to each model can be found in Table 2.  
The VAE-C model gives better results for all datasets compared to the PConv and GConv models. On average, the VAE-C models showed better values with an MSE of 3.3, PSNR of 2.1, and SSIM of 5.8 compared to the PConv model, and an MSE of 7.6, PSNR of 3.4, and SSIM of 9.4 compared to the GConv model. Image inpainting technologies and the VAE-C model have the characteristic in common that corrupted images can be restored. However, the restored image which has the most similar form to the original image is that produced by the VAE-C model.  
Figure 12: Using the Places2 dataset [39], CelebA (celebrity faces attributes) face dataset [40], and Cifar-10 (Canadian Institute For Advanced Research-10) dataset [41], the results of restoring corrupted images (**a**) with the inpainting model (**b**,**c**) and the VAE-C model (**d**) are shown. The bottom Cifar-10 dataset represents the result of using a low-resolution image, (**e**) is ground-truth. The red area shows some areas enlarged to see the restored results in more detail.