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Structure Adaptive Total Variation Minimization-based Image Decomposition
- Structure Adaptive Total Variation Minimization-based Image Decomposition
- Song J.; Cho H.; Yoon J.; Yoon S.M.
- Ewha Authors
- SCOPUS Author ID
- Issue Date
- Journal Title
- IEEE Transactions on Circuits and Systems for Video Technology
- Image decomposition; Image edge detection; Image enhancement; Minimization; Robustness; Smoothing methods; Total Variation minimization; Transforms; TV
- Institute of Electrical and Electronics Engineers Inc.
- SCI; SCIE; SCOPUS
- 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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