Face Super-Resolution Through Dual-Identity Constraint

Recently, most existing face SR methods only focus on generating pleasant texture details, even artifacts. Identity information is an important high-level face attribute, which is often ignored in the low-level super-resolution (SR) task. In view of this, we propose a dual-identity constraint dual-loop network (DIDnet), which employs identity information to constrain the SR model. First, the proposed framework consists of two closed-loop networks: one of the networks is used for generating high resolution (HR) images for exploring identity-preserving in HR feature space and the other one can learn degradation process for utilizing low resolution (LR) identity information. Furthermore, we integrate dual-identity constraints together for rendering characteristic facial images. Extensive experimental results are conducted on face databases and real-world data, which confirmed that the proposed DIDnet consistently and significantly improves both objective and subjective facial image reconstruction performances.