ATMFN: Adaptive-Threshold-Based Multi-Model Fusion Network for Compressed Face Hallucination

Although tremendous strides have been recently made in face hallucination, exiting methods based on a single deep learning framework can hardly satisfactorily provide fine facial features from tiny faces under complex degradation. This article advocates an adaptive-threshold-based multi-model fusion network (ATMFN) for compressed face hallucination, which unifies different deep learning models to take advantages of their respective learning merits. First of all, we construct CNN-, GAN- and RNN-based underlying super-resolvers to produce candidate SR results. Further, the attention subnetwork is proposed to learn the individual fusion weight matrices capturing the most informative components of the candidate SR faces. Particularly, the hyper-parameters of the fusion matrices and the underlying networks are optimized together in an end-to-end manner to drive them for collaborative learning. Finally, a threshold-based fusion and reconstruction module is employed to exploit the candidates' complementarity and thus generate high-quality face images. Extensive experiments on benchmark face datasets and real-world samples show that our model outperforms the state-of-the-art SR methods in terms of quantitative indicators and visual effects. The code and configurations are released at https://github.com/kuihua/ATMFN.