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Deep learning based image completion methods are generally based on three technologies, namely the auto encoder based method, the generation of adversarial networks based method and the recurrent network based method. However, many methods have very single results. In order to obtain the diversity of the completion results, this paper proposes a neural image completion method based on label differentiation, called LD-PICNet (Label Differentiation PICNet). This method can not only generate a complete image with clear and good semantic information, but also actively edit the tags on the generated results to maximize the diversity of the output. Specifically, this paper introduces an auxiliary classifier, which uses a single label of the image ground truth to reconstruct the image, and uses the label to actively increase the difference of the latent vectors during image completion to achieve variability of output. In addition, this paper also introduces a depth-weighted loss function based on information entropy. Different data sets are used to conduct experiments. The model’s ability to complete different types of targets is tested. The results show that this method has the ability to generate diversified results, and it has higher clarity than the other methods.

Neural Image Completion Based on Label Differentiation