Recovering Extremely Degraded Faces by Joint SuperResolution and Facial Composite In the past a few years, we witnessed rapid advancement in face superresolution from very low resolution(VLR) images. However, most of the previous studies focus on solving such problem without explicitly considering the impact of severe reallife image degradation (e.g. blur and noise). We can show that robustly recover details from VLR images is a task beyond the ability of current stateoftheart method. In this paper, we borrow ideas from facial composite and propose an alternative approach to tackle this problem. We endow the degraded VLR images with additional cues by integrating existing face components from multiple reference images into a novel learning pipeline with both low level and high level semantic loss function as well as a specialized adversarial based training scheme. We show that our method is able to effectively and robustly restore relevant facial details from 16x16 images with extreme degradation. We also tested our approach against reallife images and our method performs favorably against previous methods.

Blind Single Image Superresolution with a Mixture of Deep Networks Abstract   Existing deep neural network based image superresolution (SR) methods are mostly designed for nonblind cases, where the blur kernel used to generate the lowresolution (LR) images is assumed to be known and fixed. However, this assumption does not hold in many real scenarios. Motivated by the observation that SR of LR images generated by different blur kernels are essentially different but also correlated, we propose a mixture model of deep networks, which is capable of clustering SR tasks of different blur kernels into a set of groups. Each group is composed of correlated SR tasks with similar blur kernels and can be effectively handled by a combination of specific networks in the mixture model. To achieve automatic SR tasks clustering and network selection, we model the blur kernel with a latent variable, which is inferred from the input image by an encoder network. Since the groundtruth of the latent variable is unknown in the training stage, we initialize the encoder network by pretraining it on the blur kernel classification task to avoid trivial solutions. To jointly train the mixture model and the encoder network, we further derive a lower bound of the likelihood function, which circumvents the intractability in direct maximum likelihood estimation. Extensive evaluations are performed on benchmark data sets and validate the effectiveness of the proposed method.