Imagining the Unimaginable Faces by Deconvolutional Networks.

We tackle the challenge of constructing 64 pixels for each individual pixel of a thumbnail face image. We show that such an aggressive super-resolution objective can be attained by taking advantage of the global context and making the best use of the prior information portrayed by the image class. Our input image is so small (e.g., pixels) that it can be considered as a patch of itself. Thus, conventional patch-matching-based super-resolution solutions are unsuitable. In order to enhance the resolution while enforcing the global context, we incorporate a pixel-wise appearance similarity objective into a deconvolutional neural network, which allows efficient learning of mappings between low-resolution input images and their high-resolution counterparts in the training data set. Furthermore, the deconvolutional network blends the learned high-resolution constituent parts in an authentic manner, where the face structure is naturally imposed and the global context is preserved. To account for the possible artifacts in upsampled feature maps, we employ a sub-network composed of additional convolutional layers. During training, we use roughly aligned images (only eye locations), yet demonstrate that our network has the capacity to super-resolve face images regardless of pose and facial expression variations. This significantly reduces the requirement of precisely face alignments in the data set. Owing to the network topology we apply, our method is robust to translational misalignments. In addition, our method is able to upsample rotational unaligned faces with data augmentation. Our extensive experimental analysis manifests that our method achieves more appealing and superior results than the state of the art.