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JBE, vol. 23, no. 5, pp.614-621, September, 2018
Depth Image Restoration Using Generative Adversarial Network
John Junyeop Nah, Chang Hun Sim, and In Kyu Park
C.A E-mail: firstname.lastname@example.org
This paper proposes a method of restoring corrupted depth image captured by depth camera through unsupervised learning using generative adversarial network (GAN). The proposed method generates restored face depth images using 3D morphable model convolutional neural network (3DMM CNN) with large-scale CelebFaces Attribute (CelebA) and FaceWarehouse dataset for training deep convolutional generative adversarial network (DCGAN). The generator and discriminator equip with Wasserstein distance for loss function by utilizing minimax game. Then the DCGAN restore the loss of captured facial depth images by performing another learning procedure using trained generator and new loss function.
Keyword: Deep learning, generative adversarial network, depth image, depth camera, restoration
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