We propose a two-stage method for face hallucination. First, we generate facial components of the input image using CNNs. These components represent the basic facial structures. Second, we synthesize fine-grained facial structures from high resolution training images. The details of these structures are transferred into facial components for enhancement. Therefore, we generate facial components to approximate ground truth global appearance in the first stage and enhance them through recovering details in the second stage. The experiments demonstrate that our method performs favorably against state-of-the-art methods 1 .
Song et al. REF proposed a two-stage method that can generate facial components using CNN and then reconstructed the HR facial image through the component enhancement method.
Learning to Hallucinate Face Images via Component Generation and Enhancement
ArXiv
ArXiv
Computer Science