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|a eng
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|a Min, Dongbo
|e verfasserin
|4 aut
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|a Fast global image smoothing based on weighted least squares
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|c 2014
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|a Date Completed 30.03.2015
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|a Date Revised 02.02.2015
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a This paper presents an efficient technique for performing a spatially inhomogeneous edge-preserving image smoothing, called fast global smoother. Focusing on sparse Laplacian matrices consisting of a data term and a prior term (typically defined using four or eight neighbors for 2D image), our approach efficiently solves such global objective functions. In particular, we approximate the solution of the memory-and computation-intensive large linear system, defined over a d-dimensional spatial domain, by solving a sequence of 1D subsystems. Our separable implementation enables applying a linear-time tridiagonal matrix algorithm to solve d three-point Laplacian matrices iteratively. Our approach combines the best of two paradigms, i.e., efficient edge-preserving filters and optimization-based smoothing. Our method has a comparable runtime to the fast edge-preserving filters, but its global optimization formulation overcomes many limitations of the local filtering approaches. Our method also achieves high-quality results as the state-of-the-art optimization-based techniques, but runs ∼10-30 times faster. Besides, considering the flexibility in defining an objective function, we further propose generalized fast algorithms that perform Lγ norm smoothing (0 < γ < 2) and support an aggregated (robust) data term for handling imprecise data constraints. We demonstrate the effectiveness and efficiency of our techniques in a range of image processing and computer graphics applications
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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|a Choi, Sunghwan
|e verfasserin
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| 700 |
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|a Lu, Jiangbo
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| 700 |
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|a Ham, Bumsub
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|a Sohn, Kwanghoon
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|a Do, Minh N
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|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 23(2014), 12 vom: 04. Dez., Seite 5638-53
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