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Researchers have devoted great efforts to image dehazing with prior assumptions in the past decade. Recently developed example-based approaches typically lack elegant models for the hazy process and meanwhile demand synthetic hazy images by manual selection. The priors from observations, and those trained from synthetic images cannot always reflect true structural information of natural images in...
This paper considers single image super-resolution (SISR), which is an important low-level vision task and has various applications in multimedia society. Recently, deep neural networks have archived good performance on this field. But most of existing deep models are based on the fully data-dependent network architecture, thus missing majority of domain-knowledge of the super-resolution task. To...
Facial analysis plays very important role in many vision applications, such as authentication and entertainments. The very early works in the 1990s mostly focus on estimating geometric deformations of facial landmarks to address this task. While in the past several years, more and more efforts have been made to directly learn an appearance regression for facial analysis. Though training regressions...
Anisotropic propagations have been widely used for image processing for decades. However, most previous anisotropic propagations are simply defined on regular image pixels and cannot be used for complex vision task, e.g., object tracking. Visual tracking is a fundamental task in computer vision. In this paper, we proposed a novel discriminative anisotropic propagations model called sequential heat...
Facial feature extraction plays an important role in many multimedia and vision applications. Recent regression methods for extraction lack the explicit shape constraints for faces, and require a large number of facial images covering great appearance variations. This paper introduces a novel projective invariant, named characteristic number (CN), to explicitly characterize the intrinsic geometries...
Matrix factorization techniques have been frequently utilized in pattern recognition and machine learning. Among them, Non-negative Matrix Factorization (NMF) has received considerable attention because it represents the naturally occurring data by parts of it. On the other hand, from the geometric perspective, the data is usually sampled from a low dimensional manifold embedded in a high dimensional...
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 features for global faces. These features are considered...
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