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Many machine learning and signal processing problems can be formulated as linearly constrained convex programs, which could be efficiently solved by the alternating direction method (ADM). However, usually the subproblems in ADM are easily solvable only when the linear mappings in the constraints are identities. To address this issue, we propose a(More)
Higher-order low-rank tensors naturally arise in many applications including hyperspectral data recovery, video inpainting, seismic data reconstruction, and so on. We propose a new model to recover a low-rank tensor by simultaneously performing low-rank matrix factorizations to the all-mode matricizations of the underlying tensor. An alternating(More)
We propose a simple yet effective L<sub>0</sub>-regularized prior based on intensity and gradient for text image deblurring. The proposed image prior is motivated by observing distinct properties of text images. Based on this prior, we develop an efficient optimization method to generate reliable intermediate results for kernel estimation. The proposed(More)
This paper studies the problem of learning robust regression for real world head pose estimation. The performance and applicability of traditional regression methods in real world head pose estimation are limited by a lack of robustness to outlying or corrupted observations. By introducing low-rank and sparse regularizations, we propose a novel regression(More)
Benefiting from its effectiveness in subspace segmentation, low-rank representation (LRR) and its variations have many applications in computer vision and pattern recognition, such as motion segmentation, image segmentation, saliency detection, and semisupervised learning. It is known that the standard LRR can only work well under the assumption that all(More)
a r t i c l e i n f o Many computer vision and image processing problems can be posed as solving partial differential equations (PDEs). However, designing a PDE system usually requires high mathematical skills and good insight into the problems. In this paper, we consider designing PDEs for various problems arising in computer vision and image processing in(More)
Nonlocal mean (NM) is an efficient method for many low-level image processing tasks. However, it is challenging to directly utilize NM for saliency detection. This is because that conventional NM method can only extract the structure of the image itself and is based on regular pixel-level graph. However, saliency detection usually requires human perceptions(More)