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Structure Adaptive Total Variation Minimization-Based Image Decomposition
- Title
- Structure Adaptive Total Variation Minimization-Based Image Decomposition
- Authors
- Song, Jinjoo; Cho, Heeryon; Yoon, Jungho; Yoon, Sang Min
- Ewha Authors
- 윤정호
- SCOPUS Author ID
- 윤정호
- Issue Date
- 2018
- Journal Title
- IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
- ISSN
- 1051-8215
1558-2205
- Citation
- IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY vol. 28, no. 9, pp. 2164 - 2176
- Keywords
- Image decomposition; image enhancement; total variation minimization
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Indexed
- SCIE; SCOPUS
- Document Type
- Article
- 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 TV (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 experimental 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.
- DOI
- 10.1109/TCSVT.2017.2717542
- Appears in Collections:
- 자연과학대학 > 수학전공 > Journal papers
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