Objective Image inpainting models based on generative adversarial networks GANs suffer from deficiencies when repairing irregular and extensively damaged images including inaccurate restoration of fine image details unsatisfactory visual effects and training instability. In this regard a Wasserstein GAN-based image inpainting model with dual discriminators was proposed. Methods This method employed a convolutional neural network CNN with an encoder- decoder architecture as the generator with skip connections added between the encoder and decoder of the generator to better learn subtle image features and enhance the final inpainting results. Additionally a local discriminator network was introduced on the basis of the global discriminator to ensure consistency between locally restored areas and their surroundings. Wasserstein distance was incorporated into the discriminators to stabilize the training process and achieve more natural image inpainting. Furthermore a VGG16 feature extractor was designed to introduce perceptual and style losses to improve image inpainting by incorporating multiple loss functions. Results Comparative analysis on public datasets of faces scenes and streets showed qualitative and quantitative superiority of the proposed method over baseline models with higher performance evaluation metrics. Conclusion Experimental results demonstrate that the proposed method presents better visual effects and clearer restoration when repairing images with irregular and extensive damaged areas.