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Fast Domain Decomposition for Global Image Smoothing
- Title
- Fast Domain Decomposition for Global Image Smoothing
- Authors
- Kim, Youngjung; Min, Dongbo; Ham, Bumsub; Sohn, Kwanghoon
- Ewha Authors
- 민동보
- SCOPUS Author ID
- 민동보
- Issue Date
- 2017
- Journal Title
- IEEE TRANSACTIONS ON IMAGE PROCESSING
- ISSN
- 1057-7149
1941-0042
- Citation
- IEEE TRANSACTIONS ON IMAGE PROCESSING vol. 26, no. 8, pp. 4079 - 4091
- Keywords
- Edge-preserving image smoothing; joint image filtering; weighted-least squares; alternating minimization; majorization-minimization algorithm
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Indexed
- SCIE; SCOPUS
- Document Type
- Article
- Abstract
- Edge-preserving smoothing (EPS) can be formulated as minimizing an objective function that consists of data and regularization terms. At the price of high-computational cost, this global EPS approach is more robust and versatile than a local one that typically has a form of weighted averaging. In this paper, we introduce an efficient decomposition-based method for global EPS that minimizes the objective function of L-2 data and (possibly non-smooth and non-convex) regularization terms in linear time. Different from previous decomposition-based methods, which require solving a large linear system, our approach solves an equivalent constrained optimization problem, resulting in a sequence of 1-D sub-problems. This enables applying fast linear time solver for weighted-least squares and -L-1 smoothing problems. An alternating direction method of multipliers algorithm is adopted to guarantee fast convergence. Our method is fully parallelizable, and its runtime is even comparable to the state-of-the-art local EPS approaches. We also propose a family of fast majorization-minimization algorithms that minimize an objective with non-convex regularization terms. Experimental results demonstrate the effectiveness and flexibility of our approach in a range of image processing and computational photography applications.
- DOI
- 10.1109/TIP.2017.2710621
- Appears in Collections:
- 인공지능대학 > 컴퓨터공학과 > Journal papers
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