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 8x. In this paper, we propose an effective twostep 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.