Abstract:The current depth learning network models based on generative adversarial networks encounter artifacts, texture detail degradation, and other phenomena when facing more complex features, which leads to a visual deficiency. In order to solve these problems, an improved image inpainting algorithm combining a generative adversarial network with a coherent semantic attention mechanism is proposed. First of all, the generator adopts a two-stage inpainting method, uses gated convolution to replace the ordinary convolution of the generative adversarial network, and introduces residual blocks to solve the gradient vanishing problem and a coherent semantic attention mechanism to enhance the generator's attention to the important information and structure in the image. Secondly, Markov discriminator is adopted to enhance the network's discrimination effect, and the output results of the generator are processed by deconvolution to get the final repaired image. By comparing the inpainting results and image quality evaluation indicators with the baseline algorithm, the experimental results show that the algorithm can better predict the missing parts and improve the inpainting effect.