Organ-Branched CNN for Robust Face Super-Resolution

In this paper, we present a novel organ-branched CNN method for face super-resolution, named OBC-FSR. It is the first work focusing on facial-part-specific face SR, which consists of a local (facial part) network and a global network. Specifically, local network enhances the five key regions of human faces separately by Wasserstein generative adversarial networks (WGAN). Simultaneously, it also predicts five key regions’ masks, namely, eyes, eyebrows, mouth, nose, and other parts. The output of the local network is obtained by merging super-resolved five key regions. In order to alleviate boundary effects and distortions in the result of local network, our proposed network also includes a global network, which learns the direct mapping between LR and HR human faces. The final HR result of our FSR method is a fusion of the out-puts of local and global networks. Experimental results verify the superior performance of our method compared to the state-of-the-art.