Cooperative face hallucination using multiple references

This paper proposes a cooperative example-based face hallucination method using multiple references. The proposed method first uses clustering and residual prototype faces construction to improve the performance of hallucinating a single low-resolution (LR) face to obtain a high-resolution (HR) counterpart. In the case that multiple LR face images for a person are available, a unique feature of the proposed method is to cooperatively enhance the qualities of hallucinated HR images by taking into account the multiple input face images jointly as prior models. Experimental results demonstrate that the proposed cooperative method achieve significant subjective and objective improvement over single-prior schemes.