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Image inpainting is an essential issue in the field of computer vision. The basic purpose is to automatically recover the lost content according to the known content in the image. Although significant progress has been made in the completion of the regular missing image, the completion of the irregular image is still challenging. The completed images generated by the previous methods were different from the surrounding areas, considering the low efficiency of convolution in processing spatial position information. Based on this study, a partial convolution attention mechanism for image inpainting was proposed (PCNet), and an attention mechanism was introduced to extract long-distance and irregular image content which is a self-supervised module capable of focusing on global information. In addition, attentive normalization is introduced to model the long-distance relationship on the conditional image generation task. Experiments show that the results generated by our method are more natural and real, and the completed parts demonstrate more connected consistency.

PCNet: partial convolution attention mechanism for image inpainting