Parsing Map Guided Multi-Scale Attention Network For Face Hallucination

Chenyang Wang; Zhiwei Zhong; Junjun Jiang; Deming Zhai; Xianming Liu

ICASSP 2020accepted0 citations

Parsing Map Guided Multi-Scale Attention Network For Face Hallucination

Chenyang Wang, Zhiwei Zhong, Junjun Jiang, Deming Zhai, Xianming Liu

Abstract

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×. 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.

BibTeX

@inproceedings{icassp2020_parsingmapguided,
  title = {Parsing Map Guided Multi-Scale Attention Network For Face Hallucination},
  author = {Chenyang Wang and Zhiwei Zhong and Junjun Jiang and Deming Zhai and Xianming Liu},
  booktitle = {ICASSP 2020},
  year = {2020}
}