Parsing Map Guided Multi-Scale Attention Network For Face Hallucination

Face hallucination that aims to transform a low-resolution (LR) face image to a high-resolution (HR) one is an active domain-specific image super-resolution problem. The performance of existing methods is usually not satisfactory, especially when the upscaling factor is large, such as $8\times$. In this paper, we propose an effective two-step face hallucination method based on a deep neural network with multi-scale channel and spatial attention mechanism. Specifically, we develop a ParsingNet to extract the prior knowledge of an input LR face, which is then fed into a carefully designed FishSRNet to recover the target HR face. Experimental results demonstrate that our method outperforms the state-of-the-arts in terms of quantitative metrics and visual quality.