The existing image repair methods have some problems such as obvious trace, semantic discontinuity, unclear, etc. To solve these problems, this study proposes an image repair method based on a new encoder and context-aware loss. In this paper, the generative adversarial network is adopted as the basic network architecture. In order to fully learn the image features and get clearer repair results, SE-ResNet is introduced to extract the effective features of the image. At the same time, the joint context-aware loss training generating network is proposed to constrain the similarity of local features, so that the repaired image is closer to the original and more real and natural. Experiments on multiple public datasets in this paper prove that the proposed method can repair the damaged images better.