# Context-Encoder-Based Image Inpainting for Ancient Chinese Silk
## 4 Experiments
### User Study  
In response to the divergence between quantitative evaluation metrics and human perceptual judgment, we conducted a user study involving 10 volunteers with expertise in textile pattern research. We employed three distinct methodologies, as follows:  
1 A sample of 25 images was randomly selected from the test dataset, each featuring a random mask. The restoration outputs of three different models were compared with the original images. The participants were instructed to select the image that appeared more visually coherent and natural from each pair. The preference rate, PRa, was calculated as the proportion of choices favoring the model-generated outcomes over the original images. 2 From the test dataset, 50 images with random masks were randomly extracted for a comparative analysis of three distinct model-generated restorations. The participants were requested to identify the most visually coherent and natural image among the trio. The preference rate, PRb, represents the frequency at which the participants selected the optimal restoration from the three models presented. 3 An additional set of 25 images with random masks was drawn from the test dataset for paired A/B testing. In each session, the participants were shown pairs of images produced by two different restoration methods. They were asked to choose the image they deemed more visually coherent and natural. The preference rate, PRc, quantifies the \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Models** & **(0.01, 0.1)** & **(0.1, 0.2)** & **(0.2, 0.3)** & **(0.3, 0.4)** & **(0.4, 0.5)** & **(0.5, 0.6)** & **ALL** & **Square** \\ \hline \multirow{3}{*}{L1\% \(\mathbb{I}\)} & LISK & 0.87 & 2.22 & 4.04 & 6.18 & 8.67 & 12.55 & 5.66 & 5.92 \\  & MEDFE & 0.73 & 1.72 & 3.01 & 4.44 & 5.99 & 8.27 & 3.96 & 3.87 \\  & MADF & **0.49** & **1.35** & **2.46** & **3.71** & **5.04** & **7.06** & **3.26** & **3.43** \\ \hline \multirow{3}{*}{PSNR \(\uparrow\)} & LISK & **35.19** & **30.49** & **27.56** & **25.31** & **23.55** & **21.48** & **27.40** & **24.91** \\  & MEDFE & 32.60 & 27.28 & 24.34 & 22.28 & 20.76 & 18.92 & 24.47 & 22.59 \\  & MADF & 34.24 & 28.57 & 25.46 & 23.32 & 21.80 & 20.01 & 25.73 & 23.11 \\ \hline \multirow{3}{*}{SSIM \(\uparrow\)} & LISK & 0.9752 & **0.9344** & **0.8779** & **0.8124** & **0.7402** & **0.6391** & **0.8322** & **0.8263** \\  & MEDFE & 0.9703 & 0.9142 & 0.8374 & 0.7485 & 0.6510 & 0.5001 & 0.7718 & 0.7253 \\  & MADF & **0.9775** & 0.9325 & 0.8685 & 0.7919 & 0.7055 & 0.5565 & 0.8082 & 0.7405 \\ \hline \multirow{3}{*}{FID \(\mathbb{I}\)} & LISK & 7.77 & 15.51 & 24.26 & 36.19 & 51.32 & 83.80 & 21.26 & 35.54 \\  & MEDFE & 6.77 & 18.94 & 37.09 & 61.10 & 87.47 & 126.92 & 36.51 & 45.67 \\  & MADF & **2.06** & **6.12** & **12.35** & **21.34** & **33.41** & **54.72** & **10.43** & **12.22** \\ \hline \multirow{3}{*}{UQI \(\uparrow\)} & LISK & 0.9895 & 0.9864 & 0.9824 & 0.9749 & 0.9652 & 0.9472 & 0.9755 & **0.9748** \\  & MEDFE & 0.9960 & 0.9888 & 0.9789 & 0.9671 & 0.9542 & 0.9331 & 0.9704 & 0.9701 \\  & MADF & **0.9974** & **0.9922** & **0.9849** & **0.9758** & **0.9658** & **0.9480** & **0.9782** & 0.9737 \\ \hline \multirow{3}{*}{VIF \(\uparrow\)} & LISK & 0.9011 & 0.8376 & 0.7666 & 0.6755 & 0.5754 & 0.4487 & 0.7060 & 0.7164 \\  & MEDFE & 0.9496 & 0.8545 & 0.7454 & 0.6369 & 0.5367 & 0.4192 & 0.6939 & 0.7150 \\ \cline{1-1}  & MADF & **0.9612** & **0.8912** & **0.8012** & **0.7038** & **0.6085** & **0.4778** & **0.7437** & **0.7326** \\ \hline \hline \end{tabular} Note. “ALL” indicates measuring in the whole irregular random masks set. “Square” means square random masks measuring \(128\times 128\) pixels. \(\mathbb{I}\) means lower is better; \(\mathbb{I}\) means higher is better.  
\end{table} 
Table 1: Quantitative comparison results. proportion of participant selections favoring each of the three model-generated outcomes. The results are summarized in Table 2.  
The data presented in the table demonstrate that the MADF method exhibits superior performance across three distinct investigative approaches.