Globally and Locally Consistent Image Completion

The authors introduce a novel approach for image completion that results in images that are both locally and globally consistent. A fully-convolutional neural network it is possible to complete images of arbitrary resolutions by filling-in missing regions of any shape. To train this image completion network to be consistent, global and local context discriminators that are trained to distinguish real images from completed ones are used.


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