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RASL: Robust Alignment by Sparse and Low-rank Decomposition for Linearly Correlated Images (2010)

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by Yigang Peng , Arvind Ganesh , John Wright , Wenli Xu , Yi Ma
Citations:161 - 6 self
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BibTeX

@MISC{Peng10rasl:robust,
    author = {Yigang Peng and Arvind Ganesh and John Wright and Wenli Xu and Yi Ma},
    title = {RASL: Robust Alignment by Sparse and Low-rank Decomposition for Linearly Correlated Images},
    year = {2010}
}

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Abstract

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. We reduce this extremely challenging optimization problem to a sequence of convex programs that minimize the sum of ℓ1-norm and nuclear norm of the two component matrices, which can be efficiently solved by scalable convex optimization techniques with guaranteed fast convergence. We verify the efficacy of the proposed robust alignment algorithm with extensive experiments with both controlled and uncontrolled real data, demonstrating higher accuracy and efficiency than existing methods over a wide range of realistic misalignments and corruptions.

Keyphrases

low-rank decomposition    robust alignment    linearly correlated image    recovered aligned image    uncontrolled real data    realistic misalignment    scalable convex optimization technique    sparse matrix    robust alignment algorithm    component matrix    low-rank matrix    wide range    transformed image    gross corruption    nuclear norm    paper study    convex program    extensive experiment    image domain transformation    optimal set    challenging optimization problem    fast convergence   

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