Image Completer

Recognize and extract faces in an image and complete the corrupted portions.

Image Resolution Enhancer








Overview


This model fills in missing or corrupted parts of an image. This model uses Deep Convolutional Generative Adversarial Networks (DCGAN) to fill the missing regions in an image. The model is trained using celebA dataset and works best for completing corrupted portions in human face. Input to the model is an image with corrupted face. OpenFace face recognition tool will detect and extract the corrupted face from the input image. This extracted face is then given to OpenFace alignment tool where it is aligned (inner eyes with bottom lip) and resized (64 x 64) producing an output that can be used by the model to complete the corrupted portions. The output is a collage of 20 images, in a 4×5 grid, representing the intermediate results and final completed image (bottom-right). The model is based on the Tensorflow implementation of DCGAN.



References :

Raymond A. Yeh, Chen Chen, Teck Yian Lim, Alexander G. Schwing, Mark Hasegawa-Johnson, Minh N. Do, “Semantic Image Inpainting with Deep Generative Models”, CVPR 2017.DCGAN Tensorflow for image completion Github RepoOpenFace for facial recognition




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Official
incubator

Coordination : Hassan Jacobsen