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Principal component analysis is a fundamental operation in computational data analysis, with myriad applications ranging from web search to bioinformatics to computer vision and image analysis. However, its performance and applicability in real scenarios are limited by a lack of robustness to outlying or corrupted observations. This paper considers the(More)
This paper studies the problem of simultaneously aligning a batch of linearly correlated images despite gross corruption (such as occlusion). Our method seeks an optimal set of image domain transformations such that the matrix of transformed images can be decomposed as the sum of a sparse matrix of errors and a low-rank matrix of recovered aligned images.(More)
In this paper, we propose a reweighted low-rank matrix recovery method and demonstrate its application for robust image restoration. In the literature, principal component pursuit solves low-rank matrix recovery problem via a convex program of mixed nuclear norm and l1 norm. Inspired by reweighted l1 minimization for sparsity enhancement, we propose(More)
We consider the problem of super-resolution from unregistered aliased images with unknown spatial scaling factors and shifts. Due to the limitation of pixel size in the image sensor, the sampling rate for each image is lower than the Nyquist rate of the scene. Thus, we have aliasing in captured images, which makes it hard to register the low-resolution(More)
In this paper, we propose a novel face recognition method based on anisotropic dual-tree complex wavelet packets(ADT-CWP). 2-D dual-tree complex wavelet transform(DT-CWT) provides a geometrically oriented decomposition for image representation as well as shift invariance. By applying anisotropic wavelet packet decomposition on DT-CWT further, ADT-CWP can be(More)
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