Structure Adaptive Total Variation Minimization-based Image Decomposition
- 주제(키워드) Image decomposition , Image decomposition , Image edge detection , Image enhancement , Minimization , Robustness , Smoothing methods , Total Variation minimization , Transforms , TV
- 관리정보기술 faculty
- 등재 SCIE, SCOPUS
- 발행기관 Institute of Electrical and Electronics Engineers Inc.
- 발행년도 2017
- URI http://www.dcollection.net/handler/ewha/000000146958
- 본문언어 영어
- Published As http://dx.doi.org/10.1109/TCSVT.2017.2717542
초록/요약
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
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