The work aims to propose an image repair algorithm based on generative adversarial network, so as to ef-fectively solve the difficulties in preserving cultural relic images and repairing them in physical method. The proposed algorithm was mainly divided into two stages. In the first stage, the edge information of the known part of the image was extracted by Canny edge detector, and a generator and a discriminator were used to repair the missing edges of the image. In the second stage, the edges generated in the first stage were taken as prior information, and the missing part of the image was repaired by one generator and two discriminators. The two discriminators consisted of a global discriminator and a local discriminator. The global discriminator was used to evaluate the overall coherence of the repaired image. The local discriminator was used to check the local consistency of a small area centered on the area to be repaired. Compared with the traditional algorithm, the proposed algorithm ensured the consistency of the global semantic structure by improving the quality of the generated image texture. In terms of objective indicators (peak signal-to-noise ratio and structural similarity), the results of the proposed algorithm were better than those of other methods. The algorithm proposed herein can effectively repair the defective parts of cultural relic images, especially large-scale missing complex structures, and has achieved good visual effects, indicating that the algorithm has good repair performance.