# Image Inpainting Based on Multi-Patch Match with Adaptive Size
## 4 Experimental Results
### Image Restoration
#### 4.1.2 Batch Test  
In order to evaluate the performance of these algorithms on a broader level, our next experiment is conducted with more test samples. We randomly select 100 images from the public dataset Places2 [24] and resize them to \(512\times 512\). Then we make 20 masks to randomly generate missing areas on those sample images. The aforementioned methods are tested, and their objective quantitative evaluation indicators: PSNR, SSIM, and running time are plotted in Figures 11(a), 12(a) and 13(a), respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Image** & **CDD [4]** & **[PERSON]’s [10]** & **[PERSON]’s [17]** & **[PERSON]’s [18]** & **Proposed** \\ \hline Lena (\(512\times 512\)) & 38.4142 & 35.0791 & 34.8686 & 35.5572 & 37.8445 \\ House (\(512\times 512\)) & 33.7388 & 34.4852 & 35.289 & 35.089 & 38.3214 \\ Sculpture (\(256\times 256\)) & 23.6974 & 24.0135 & 23.2412 & 23.3735 & 27.2959 \\ Texture I (\(640\times 640\)) & 22.4238 & 22.4142 & 22.4722 & 21.0718 & 24.1134 \\ Texture II (\(640\times 640\)) & 27.0897 & 28.4736 & 28.5973 & 26.2799 & 29.2464 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Peak signal to noise ratio (PSNR) (dB) comparison of Figure 7, Figure 8, Figure 9, Figure 10 and Figure 11. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Image** & **CDD [4]** & **[PERSON]’s [10]** & **[PERSON]’s [17]** & **[PERSON]’s [18]** & **Proposed** \\ \hline Lena (\(512\times 512\)) & 0.9873 & 0.9775 & 0.9752 & 0.9752 & 0.9845 \\ House (\(512\times 512\)) & 0.9664 & 0.9807 & 0.9813 & 0.9823 & 0.9856 \\ Sculpture (\(256\times 256\)) & 0.8976 & 0.8928 & 0.8828 & 0.8808 & 0.9010 \\ Texture I (\(640\times 640\)) & 0.8686 & 0.8326 & 0.8323 & 0.7958 & 0.8780 \\ Texture II (\(640\times 640\)) & 0.8566 & 0.8930 & 0.8969 & 0.8347 & 0.9030 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Structural similarity index (SSIM) comparison of Figure 7, Figure 8, Figure 9, Figure 10 and Figure 11. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Image** & **CDD [4]** & **Criminisi’s [10]** & **Liu’s [17]** & **Zhou’s [18]** & **Proposed** \\ \hline Lena (\(512\times 512\)) & 0.9873 & 0.9775 & 0.9752 & 0.9752 & 0.9845 \\ House (\(512\times 512\)) & 0.9664 & 0.9807 & 0.9813 & 0.9823 & 0.9856 \\ Sculpture (\(256\times 256\)) & 0.8976 & 0.8928 & 0.8828 & 0.8808 & 0.9010 \\ Texture I (\(640\times 640\)) & 0.8686 & 0.8326 & 0.8323 & 0.7958 & 0.8780 \\ Texture II (\(640\times 640\)) & 0.8566 & 0.8930 & 0.8969 & 0.8347 & 0.9030 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Running time (s) comparison of Figure 7, Figure 8, Figure 9, Figure 10 and Figure 11.  
We also study the performance of these methods in relation to the percentage of the masked area from above 100 samples. These samples are divided into five categories with respect to their mask ratios. Mask ratios are divided into five intervals vary from 0% to 25% with the step of 5%. Figures 12b, 13b and 14b show how the mask ratios affect the mean PSNR, SSIM, and running time of each method. It can be learned from Figures 12a and 13a that PSNR and SSIM curves are very close that are not so distinguishable. The reason is that the differences between the restored images are those masked areas only, which are much smaller than the source region that is exactly the same as the original image. However, it still can be seen that, for most images, the proposed method has obtained relatively higher PSNR and SSIM values than other methods. In fact, our method achieves the highest PSNR  
Figure 14: Comparison of running time for different methods. (**a**) Running time of 100 inpainted results; (**b**) Effect of mask ratios on the mean running time. Figure 12: Comparison of PSNR for different methods: (**a**) PSNR of 100 inpainted results; (**b**) effect of mask ratios on the mean PSNR. Figure 13: Comparison of SSIM for different methods: (**a**) SSIM of 100 inpainted results; (**b**) effect of mask ratios on the mean SSIM.