Face super-resolution using multiple occluded images of different resolutions

In this paper, we present a novel learning-based algorithm to super-resolve multiple partially occluded low-resolution face images. By integrating hierarchical patch-wise alignment and inter-frame constraints into a Bayesian framework, we can probabilistically align multiple input images at different resolutions and recursively infer the high-resolution face image. We address the problem of fusing partial imagery information through multiple frames and discuss the new algorithm's effectiveness when encountering occluded low-re solution face images. We show promising results compared to that of existing face hallucination methods.