Face hallucination via position-based dictionaries coding in kernel feature space

In this paper, we present a new method to reconstruct a high-resolution (HR) face image from a low-resolution (LR) observation. Inspired by position-patch based face hallucination approach, we design position-based dictionaries to code image patches, and recovery HR patch using the coding coefficients as reconstruction weights. In order to capture nonlinear similarity of face features, we implicitly map the data into a high dimensional feature space. By applying kernel principal analysis (KPCA) on the mapped data in the high dimensional feature space, we can obtain reconstruction coefficients in a reduced subspace. Experimental results show that the proposed method can effectively reconstruct details of face images and outperform state-of-the-art algorithms in both quantitative and visual comparisons.