Free-Form Image Inpainting with Separable Gate Encoder-Decoder Network

Image inpainting refers to the process of reconstructing damaged areas of an image. For image inpainting, there are many means to generate not too bad inpainting results today. However, these methods either make the results look unrealistic or have complex structures and a large number of parameters. In order to solve the above problems, this paper designed a simple encoder-decoder network and introduced the region normalization technique. At the same time, a new separable gate convolution is proposed. The simple network architecture and separable gate convolution significantly reduce the number of network parameters. Moreover, the separable gate convolution can learn the mask (represents the missing area) from the feature map and update it automatically. After mask update, weights will be applied to each pixel of the feature map to alleviate the impact of invalid mask information on the completed result and improve the inpainting quality. Our method reduces 0.58M parameters. Moreover, our method improved the PSNR of Celeba and Paris Street View by 0.7–1.4 dB and 0.7–1.0 dB, respectively, in 10% to 60% damage cases. The corresponding SSIM has been increased 1.6 to 2.7 and 0.9 to 2.3%.