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In this paper, we propose a novel face hallucination method to reconstruct a high-resolution face image from a lowresolution observation based on a set of highand lowresolution 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)
This letter proposes a novel single image super-resolution (SR) method based on the low-rank matrix recovery (LRMR) and neighbor embedding (NE). LRMR is used to explore the underlying structures of subspaces spanned by similar patches. Specifically, the training patches are first divided into groups. Then the LRMR technique is utilized to learn the latent(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)
Face hallucination is to reconstruct a high-resolution face image from a low-resolution one based on a set of highand 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 the(More)
Content-aware image resizing is a kind of new and effective approach for image resizing, which preserves image content well and does not cause obvious distortion when changing the aspect ratio of images. Recently, a seam based approach for content-aware image resizing was proposed by Avidan and Shamir. Their results are impressive, but because the method(More)
In this paper, we address the issue of extracting contour of the object with a specific shape. A hierarchical graphical model is proposed to represent shape variations. A complex shape is decomposed into several components which are described as principal component analysis (PCA) based models in various levels. The hierarchical representation allows for(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)