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Among image enhancement methods, histogram equalization (HE) has received the most attention because of its intuitive implementation quality, high efficiency, and the monotonicity of its intensity mapping function. However, HE is indiscriminate and overemphasizes the contrast around intensities with large pixel populations but little visual importance. To address this issue, we propose an HE-based method that adaptively controls the contrast gain according to the potential visual importance of intensities and pixels.

Depth maps captured by RGB-D cameras are often noisy and incomplete at edge regions. Most existing methods assume that there is a co-occurrence of edges in depth map and its corresponding color image, and improve the quality of depth map guided by the color image. However, when the color image is noisy or richly detailed, the high frequency artifacts will be introduced into depth map. In this paper, we propose a deep residual network based on deep fusion and local linear regularization for guided depth enhancement.

Visual context has formed a robust stimulation for visual perception. Spatio-temporal context in existing trackers sometimes shows weak reliability in visible light videos with poor quality. Supplemented by the infrared perception, this work exploits the role of visual context in tracking in a spatial-sequential-spectral view, by which to excavate dominance of different contexts in various scenarios.

In motion compensated frame interpolation, a repetition
pattern in an image makes it difficult to derive an accurate
motion vector because multiple similar local minima exist in
the search space of the matching cost for motion estimation.
In order to improve the accuracy of motion estimation in a
repetition region, this paper attempts a semi-global approach
that exploits both local and global characteristics of a
repetition region. Experimental results demonstrate that the
proposed method significantly outperforms the previous local

Image-set classification has recently generated great popularity due to widespread application to challenging tasks in computer vision. The great challenges arise from measuring the similarity between image sets which usually exhibit huge inter-class ambiguity and intra-class variation. In this paper, based on the assumption that each image set as a linear subspace can be treated as a point on a Grassmann manifold, we propose discriminant Grassmann kernels (DGK) of principal angles between subspaces.

This paper proposes a novel descriptor based on the local derivative
pattern (LDP) for 3D face recognition. Compared to the local binary
pattern (LBP), LDP can capture more detailed information by encoding
directional pattern features. It is based on the local derivative
variations that extract high-order local information. We propose a
novel discriminative facial shape descriptor, local normal derivative
pattern (LNDP) that extracts LDP from the surface normal. Using
surface normal, the orientation of a surface at each point is determined

This paper proposes an extension to the Generative Adversarial Networks (GANs), namely as ARTGAN to synthetically generate more challenging and complex images such as artwork that have abstract characteristics. This is in contrast to most of the current solutions that focused on generating natural images such as room interiors, birds, flowers