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In this paper, we propose a novel face hallucination method to reconstruct a high-resolution face image from a low-resolution observation based on a set of high-and low-resolution local training image pairs. Instead of basing on probabilistic or manifold learning models, the proposed method synthesizes the high-resolution image patch using the same position(More)
Low rank and sparse representation based methods, which make few specific assumptions about the background, have recently attracted wide attention in background modeling. With these methods, moving objects in the scene are modeled as pixel-wised sparse outliers. However, in many practical scenarios, the distributions of these moving parts are not truly(More)
Most face hallucination methods are usually limited to frontal face with small pose variations. This letter presents a simple and efficient multiview face hallucination (MFH) method to generate high-resolution (HR) multiview faces from a single given low-resolution (LR) one. The problem is addressed in two steps. A simple face transformation method is(More)
Based on the manifold assumption, some face hallucination methods have been developed. However, since the super-resolution (SR) is an ill-posed problem, the manifold assumption does not hold always. To solve this problem, we modify the assumption using Easy-Partial Least Squares (EZ-PLS) algorithm and present a new face hallucination scheme using the(More)
Recently, patch-based face hallucination methods have shown the ability for achieving high-quality face images. The high-resolution (HR) patches can be reconstructed by a linear combination of training patches, while the combination coefficients are learned according to the corresponding low-resolution (LR) patches. In order to reflect the local features,(More)
A novel two-step super-resolution (SR) method for face images is proposed in this paper. The critical issue of global face reconstruction in the two-step SR framework is to construct the relationship between high resolution (HR) and low resolution (LR) features. We choose the Principal Component Analysis (PCA) coefficients of LR/HR face images as the(More)
Face hallucination is to reconstruct a high-resolution face image from a low-resolution one based on a set of high-and low-resolution training image pairs. This paper proposes an example-based two-step face hallucination method through coefficient learning. Firstly, the low-resolution input image and the low-resolution training images are interpolated to(More)
A learning-based face hallucination method is proposed in this paper for the reconstruction of a high-resolution face image from a low-resolution observation based on a set of high-and low-resolution training image pairs. The proposed global linear modal based super-resolution estimates the optimal weights of all the low-resolution training images and a(More)