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.