# A Review of Image Inpainting Methods Based on Deep Learning
## 4 Deep-Learning-Based Image Inpainting Algorithm
### Boundary-Center Inpainting Method  
The boundary-center inpainting method can effectively eliminate central blurring. [PERSON] et al. [102] designed a cyclic feature inference network. During the current cycle, the final attention score for each pixel is obtained by weighting the attention scores from the previous cycles with the current attention score. This achieves improved inpainting results at the center of the hole. [PERSON] et al. [15] proposed a novel visual structure reconstruction (VSR) layer to entangle the reconstruction of visual structure and visual features, benefiting from shared parameters. They stacked four VSR layers repetitively in the encoding and decoding stages of a U-Net-like [61] architecture to form the generator of a generative adversarial network (GAN). According to [PERSON] et al. [103], the size of the missing area increases as training progresses. Magnification, refinement, and reduction strategies constitute a framework-agnostic method to enhance high-frequency details and can be applied to any CNN-based inpainting technique. [PERSON] et al. [104] proposed a full-resolution residual network (FRRN) for the restoration of irregular holes. [PERSON] et al. [105] divided the hole-filling process into several different stages, using an LSTM [106] framework to string all the stages together. By introducing this learning strategy, they were able to gradually reduce large damaged areas in natural images, resulting in good inpainting outcomes. Moreover, since the entire process of hole inpainting occurs in a single forward pass, the model boasts high efficiency. [PERSON] et al. [21] achieved high-quality results with optimal perceptual quality by adjusting a progressive learning scheme of a semantically aware patch-generative adversarial network (SA-Patch GAN).  
The research methodologies with respect to multistage image inpainting networks based on the boundary-center approach and their quantitative evaluations on commonly used datasets are presented in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Reference** & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** & **FID\(\downarrow\)** & **L1\(\downarrow\)/\%** & **Image Resolution** & **Mask Type** \\ \hline \multirow{3}{*}{Places2} & [30] & 21.75 & 0.8230 & 8.16 & 3.86 & \multirow{3}{*}{256 \(\times\) 256} & Regular mask (25\%) \\  & & 24.92 & 0.8610 & 4.91 & 2.59 & & Irregular mask (20–30\%) \\ \cline{2-7}  & [46] & 25.22 & 0.9026 & 7.035 & - & 256 \(\times\) 256 & Irregular mask (20–40\%) \\ \hline \multirow{3}{*}{Celeb A} & [47] & 26.82 & 0.9270 & 1.654 & 2.08 & \multirow{3}{*}{256 \(\times\) 256} & Regular mask (25\%) \\  & & 33.19 & 0.9600 & 1.227 & 1.47 & & Irregular mask \\ \cline{2-7}  & [71] & 26.28 & 0.912 & & & 256 \(\times\) 256 & Irregular mask (30–40\%) \\ \hline \multirow{3}{*}{Paris Street View} & [71] & 30.99 & 0.954 & & & 256 \(\times\) 256 & Irregular mask (10–20\%) \\  & [65] & 31.07 & & 26.32 & 1.08 & 228 \(\times\) 228 & Irregular mask (10–20\%) \\ \hline \multirow{3}{*}{Cityscapes} & [44] & 18.03 & 0.75 & 39.93 & & 256 \(\times\) 256 & Irregular mask \\ \cline{1-1}  & [85] & 34.26 & 0.96 & & 256 \(\times\) 256 & Irregular mask \\ \hline \hline \end{tabular}
\end{table}
Table 5: Quantitative assessments of multistage structure–texture inpainting methods.  
While a dual-stage image inpainting network can effectively restore image content, its high complexity and computational load are inevitable. Moreover, dual-stage image inpainting heavily relies on the results of the first stage. When the first stage does not achieve satisfactory inpainting, the second-stage network finds it challenging to carry out detailed inpainting.