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Image inpainting networks can produce visually reasonable results in the damaged regions. However, existing inpainting networks may fail to reconstruct the proper structures or tend to generate the results with color discrepancy. To solve this issue, this paper proposes an image inpainting approach using the proposed two-stage loss function. The loss function consists of different Gaussian kernels, which are utilized in different stages of network. The use of our two-stage loss function in coarse network helps to focus on the image structure, while the use of it in refinement network is helpful to restore the image details. Moreover, we proposed a global and local PatchGANs (GAN means generative adversarial network), named GL-PatchGANs, in which the global and local markovian discriminators were used to control the final results. This is beneficial to focus on the regions of interest (ROI) on different scales and tends to produce more realistic structural and textural details. We trained our network on three popular datasets on image inpainting separately, both Peak Signal to Noise ratio (PSNR) and Structural Similarity (SSIM) between our results, and ground truths on test images show that our network can achieve better performance compared with the recent works in most cases. Besides, the visual results on three datasets also show that our network can produce visual plausible results compared with the recent works.

Image Inpainting Using Two-Stage Loss Function and Global and Local Markovian Discriminators