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Structure Adaptive Total Variation Minimization-based Image Decomposition

Title
Structure Adaptive Total Variation Minimization-based Image Decomposition
Authors
Song J.Cho H.Yoon J.Yoon S.M.
Ewha Authors
윤정호
SCOPUS Author ID
윤정호scopus
Issue Date
2017
Journal Title
IEEE Transactions on Circuits and Systems for Video Technology
ISSN
1051-8215JCR Link
Keywords
Image decompositionImage edge detectionImage enhancementMinimizationRobustnessSmoothing methodsTotal Variation minimizationTransformsTV
Publisher
Institute of Electrical and Electronics Engineers Inc.
Indexed
SCI; SCIE; SCOPUS scopus
Abstract
Structure-preserving image decomposition separates a given image into structure and texture by smoothing the image, simultaneously preserving or enhancing image edges. The well-studied problem of image decomposition is applied to various areas, such as image smoothing, detail enhancement, non-photorealistic rendering, image artistic rendering, and high-dynamic-range compression. In this paper, we propose a fast algorithm for structure-preserving image decomposition that adopts total variation (TV) minimization to the moving least squares (MLS) method with non-local weights, called structure adaptive total variation (SATV) minimization. MLS with non-local weights provides high accuracy approximation that is robust to noise, and allows a fast convergence with TV regularization term. As a result, our proposed SATV preserves the dominant structure while flattening fine-scale details. The experiment results show that the SATV minimization algorithm provides faster and more robust image decomposition than the well-known previous approaches. We demonstrate the usefulness of our algorithm by presenting successful applications in image smoothing and detail enhancement. IEEE
DOI
10.1109/TCSVT.2017.2717542
Appears in Collections:
자연과학대학 > 수학전공 > Journal papers
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