| Pixel-generation-based techniques | Richard et al［35］ | 2001/VIIP | Fast image restoration method based on diffusion convolution kernel（Gaussian） |
| Hadhoud et al［36］ | 2008/SIP | The position of zero weight value of diffusion convolution kernel［35］ |
| Jain et al［37］ | 2008/NIPS | A neural network structure for denoising |
| Auto-encoder-based techniques | Xie et al［39］ | 2012/NIPS | Stacked sparse denoising Auto-encoders |
| Pathak et al［40］ | 2016/CVPR | Context encoder to capture more semantic information |
| Iizuka et al［41］ | 2017/ACM | Global and local context discriminators added to the auto-encoder |
| Yu et al［44］ | 2018/CVPR | A parallel encoder model based on attention mechanism |
| Sagong et al［45］ | 2019/CVPR | A shared encoding network with two parallel decoding tasks |
| Shin et al［46］ | 2020/NNLS | Adaptive dilated convolutional layers added to PEPSI［45］ model |
| Yang et al［47］ | 2017/CVPR | Multi-scale neural patch synthesis approach |
| Yan et al［49］ | 2018/ECCV | A special shift-connection layer Shift-Net |
| Liu et al［51］ | 2018/ECCV | A partial convolution structure based on U-Net structure |
| Xie et al［53］ | 2019/ICCV | A learnable bidirectional attention module which can automatically update the mask |
| Liu et al［54］ | 2019/ICCV | A network architecture based on coherent attention mechanism layer |
| Nazeri et al［56］ | 2019/Arxiv | A two-stage adversarial model EdgeConnect |
| Li et al［57］ | 2019/ICCV | A progressive reconstruction of visual structure network |
| Ren et al［58］ | 2019/ICCV | A novel two-stage network which can generate texture structures consistent with context semantics |
| Zeng et al［62］ | 2019/CVPR | A pyramid context encoder network combining high-level semantics and texture information |
| Yi et al［63］ | 2020/CVPR | A context residual aggregation network for high resolution image inpainting |
| Li et al［64］ | 2020/CVPR | A cyclic feature inference network for recovering the large missing regions of damaged images |
| GAN-based techniques | Radford et al［65］ | 2015/Arxiv | DCGANs combining Convolutional Neural Network（CNN）and unsupervised learning |
| Isola et al［55］ | 2017/CVPR | PatchGAN based on patch identification |
| Yeh et al［66］ | 2017/CVPR | DGMs to repair irregular regions and capture richer semantics |
| Lou et al［68］ | 2018/PRRS | RMSProp optimization algorithm is added to WGAN to maintain good performance on non-convex problems |
| Yu et al［69］ | 2019/ICCV | SN-Patch GAN network based on gated convolution to improve the details and semantic accuracy of repaired results |
| Wang et al［70］ | 2021/IEEE | The validity transfer convolution and region compound normalization modules to realize the dynamic selection of valid information |
| Zheng et al［72］ | 2019/CVPR | PIC NET to generate a variety of repair results |
| Zhao et al［73］ | 2020/CVPR | An unsupervised cross-space translation generative adversarial network |
![Structure of traditional auto-encoder[7]](/Images/icon/loading.gif){kind=icon}
