# A Review of Image Inpainting Methods Based on Deep Learning
## 8 Outlook and Challenges  
From its inception, image inpainting, especially the inpainting technique, has undergone several developmental phases. In its early stages, image restoration primarily relied on traditional image processing methods, such as those based on partial differentials, \begin{table}
\begin{tabular}{c l l l} \hline \hline
**Reference** & **Main Features** & **Advantages** & **Limitations** \\ \hline
[120] & Seven mask-generating strategies & Achieves superior results & Increases model complexity \\ \hline
[121] & Decomposition of complex mask region & Sequentially repairs each patch & Requires more computational resources \\ \hline
[21] & Progressive learning of SA-Patch GAN & Achieves high-quality results & Requires more training time \\ \hline
[110] & Unsupervised posterior estimation & Demonstrates the potential of diffusion models & Might not be suitable for all images \\ \hline
[113] & Replaces the reverse diffusion process in diffusion models & Handles irregular damage & Requires more parameters \\ \hline
[117] & Denoising process in latent space & Achieves more realistic inpainting results & Increases model complexity \\ \hline \hline \end{tabular}
\end{table} 
Table 9: _Cont._sample-based image restoration models, and variational restoration based on geometric image models. However, with the surge in computational resources and the rapid advancement of deep learning technologies, image restoration methods rooted in deep neural networks have gradually occupied the forefront of the field. These methods harness the known portions of an image and, through trained models, compute pixel information for areas awaiting restoration [127; 128]. Despite the significant strides made in image restoration technology, several challenges persist:  
1. Computational complexity: Deep learning models typically demand substantial computational resources, which might not be feasible for certain real-time applications. 2. Real-time requirements: For specific applications, such as real-time video streams or gaming, instantaneous image restoration is paramount, setting a higher bar for the technology. 3. Restoration quality: In certain intricate scenarios, prevailing techniques might fall short of achieving the desired restoration outcomes. Addressing these challenges, the future avenues for improvement encompass:  
1. Model lightweighting: The development of more streamlined models that not only ensure restoration quality but also meet real-time requirements. 2. Adaptive learning: Enabling models to adaptively learn and restore based on varying scenarios and content. 3. Multimodal fusion: Incorporating multiple sources of information (e.g., depth and semantics) for image restoration to enhance accuracy and robustness. The promise of these directions stems from their potential to tackle the core issues of current technologies while aligning with the evolving trends in computer vision and machine learning. Furthermore, most image inpainting solutions emphasize object removal or texture synthesis, implying a reliance on the undamaged image areas to match features for the damaged zones, culminating in the inpainting process. However, achieving semantic generation remains elusive. When the damaged area surpasses 70%, the dearth of known information means that various deep-learning-based image inpainting methods, including those rooted in CNNs, VAEs, and GANs, grapple with accurate image restoration. This underscores the pivotal role of the generative capability of inpainting models. While popular generative models like GANs and diffusion models have their pitfalls, such as mode collapse and unstable GAN training, research suggests that robust image generation capabilities are key to large-scale image inpainting. This has led to the proposition of constructing conditional generative models predicated on the basis of collaborative modulation, contingent on the visible area.