Inpainting method of generative adversarial network based on improved reconstructed loss function

In processing image restoration tasks, more and more researchers are using generative adversarial networks, and have made many good achievements. However, the loss function used in current repair methods does not give different weights to the losses of areas with different repair effects. Therefore, we propose a generative adversarial network repair method based on improved reconstructed loss, which assigns different weights to the losses of areas with different repair effects by improving the reconstructed loss, enhancing the repair effect on areas with poor repair effects. We will also introduce improved reconstruction loss into perception loss and style loss to enhance the restoration effect of image details. This method was tested using the Paris StreetView and Celebra datasets. From the test results, it can be seen that our PSNR and SSIM have been improved compared to the comparative method, demonstrating the effectiveness of our approach.