In recent years

deep learning based methods have shown preferable results for the task of inpainting corrupted images.However

the existing standard convolutional neural network approaches often cause problems with excessive color discrepancy

image texture loss and distortion.A deep network based image inpainting model is proposed in this paper

consisting of two generative adversarial network modules.One of the modules is used to inpaint the missing area of the image

where the generator is constituted with partial convolutions.The other module is the image optimization network

which is applied to solve the problem of local chromatic aberration after image restoration

and in which the generator is originated from the depth residual network.These two modules cooperated to improve the visual effect and image quality of inpainted images.Using MOS

SSIM and PSRN as the evaluation criteria

the experimental results of qualitative and quantitative comparisons with other state-of-the-art methods have shown that the proposed model performed better.