This paper works on recovering noised or cropped images by training a neural network based on DCGAN’s and WGAN’s structures. Various approaches have been used including the generative model using deep neural network, modified inputs for the generator, application of pre-trained classification model, etc. In this paper, the advantages of different GANs are combined to tackle the image inpainting problem. Besides, this paper proposed a special data feeding approach which concurrently trains generator and discriminator of a GAN to further improve the performance of domain specific inpainting tasks. This architecture is evaluated and tested on LSUN dataset with two different domains. The results reflect the feasibility of the approach, and comparing to the existing semantic inpainting methods, this architecture further improves both numerical loss and classification accuracy.