Coupled-layer neighbor embedding for surveillance face hallucination

As the face image captured by a surveillance camera is typically very low-resolution (LR), blurred and noisy, traditional neighbor embedding method considers only one manifold (the LR image manifold) and fails very often to reliably estimate the intention geometrical structure. In this paper, we introduce the notion of neighbor embedding from the LR image manifold and the high-resolution (HR) one simultaneously and propose a novel neighbor embedding model, termed the coupled-layer neighbor embedding (CLNE), for surveillance face hallucination. CLNE differs substantially from other neighbor embedding models in that the former has two layers: the LR layer and the the HR layer. The LR layer in this model is the local geometrical structure of the LR patch manifold, which is characterized by the reconstruction weights; the HR layer in this model is a set of HR training patches that guide the K-nearest neighbor (K-NN) searching and geometrically constrain the reconstruction weights. By this coupled constraint paradigm between the adaptation of the LR layer and the HR one, CLNE can achieve a more robust neighbor embedding through the significant degradation process. Indeed, the experimental results confirm that our method outperforms the related state-of-the-art methods by having better objective values as well as better visual results.