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Image unmosaicing without location information using stacked GAN

Image mosaicing is an image processing technique that is most commonly used to conceal identities of sensitive objects. The authors’ research features recovering the mosaiced parts in an image, especially focusing on facial parts. While recent image completion methods based on deep learning have shown promising results on recovering damaged parts in an image, they have not addressed the problem of image unmosaicing. Moreover, all those methods necessitate the location information of damaged parts to tackle the recovery problem. They formulate unmosaicing as an image-to-image translation problem, and propose a two-stage method using generative adversarial network (GAN): stage-I GAN generates a coarse prediction followed by stage-II GAN which produces a final unmosaiced image with finer information. A combination of low-level loss and high-level structural similarity loss is used to attain visually plausible and semantically consistent output. They have evaluated their method on the CelebA dataset and achieved better results than state-of-the-art image completion methods without explicitly exploiting the location information of mosaiced parts.