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A super-resolution method for low-quality face image through RBF-PLS regression and neighbor embedding

In this paper, a new two-step method is proposed to infer a high-quality and high-resolution (HR) face image from a low-quality and low-resolution (LR) observation based on training samples in the database. First, a global face image is reconstructed based on the non-linear relationship between LR and HR face images, which is established according to radial basis function and partial least squares (RBF-PLS) regression. Based on the reconstructed global face patches manifold (formed by the image patches at the same position of all global face images), whose local geometry is more consistent with that of original HR face patches manifold than noisy LR one is, the Neighbor Embedding is applied to induce the target HR face image by preserving the similar local geometry between global face patches manifold and the original HR face patches manifold. A comparison of some state-of-the-art methods shows the superiority of our method, and experiments also demonstrate the effectiveness both under simulation and real conditions.