Face hallucination scheme based on singular value content metric for K-NN selection and an iterative refining in a modified feature space

Numbers of neighbor embedding (NE) methods have been proposed, which use the image content metric based on the distance values such as Euclidean distance between the input image patch and the image patches in the training set to find the nearest neighbors. In contrast to these approaches we propose to use image content metric that uses the most effective singular values of the patch of interest. Singular value content metric give the effective and quantitative measure of the true image content and can search the most similar patches from the training set which possess the local similarity with the input patch. First we find the K most similar low resolution (LR) and corresponding high resolution (HR) patches by using the proposed image content metric. Secondly we project the K neighbor onto a modified feature space by employing easy partial least square estimation (EZ-PLS). In modified feature space we propose to explore the data structure of both LR and HR manifold and iteratively update Z nearest neighbors and reconstruction weights based on the results from previous iteration. The Rigorous experimentation with application to face hallucination demonstrate the effectiveness of the proposed method.