Face hallucination using correlative residue compensation in a modified feature space

Abstract

Local linear embedding (LLE) is a promising manifold learning method in the field of machine learning. Number of face hallucination (FH) methods have been proposed due to its neighborhood preserving nature. However, the projection of low resolution (LR) image to high resolution (HR) is “one-to-multiple” mapping; therefore manifold assumption does not hold well. To solve the above inconsistency problem we proposed a new approach. First, an intermediate HR patch is constructed based on the non linear relationship between LR and HR patches, which is established using partial least square (PLS) method. Secondly, we incorporate the correlative residue compensation to the intermediate HR results by using only the HR residue manifold. We use the same combination coefficient as for the intermediate hallucination of the first phase. Extensive experiments show that the proposed method outperforms some state-of-the-art methods in both reconstruction error and visual quality.