- Author
- Junqing Huang (UGent) , Haihui Wang, Xuechao Wang (UGent) and Michael Ruzhansky (UGent)
- Organization
- Project
- Abstract
- In this paper, we propose a semi-sparsity smoothing method based on a new sparsity-induced minimization scheme. The model is derived from the observations that semi-sparsity prior knowledge is universally applicable in situations where sparsity is not fully admitted such as in the polynomial-smoothing surfaces. We illustrate that such priors can be identified into a generalized L-0-norm minimization problem in higher-order gradient domains, giving rise to a new "feature-aware" filter with a powerful simultaneous-fitting ability in both sparse singularities (corners and salient edges) and polynomial-smoothing surfaces. Notice that a direct solver to the proposed model is not available due to the non-convexity and combinatorial nature of L-0-norm minimization. Instead, we propose to solve it approximately based on an efficient half-quadratic splitting technique. We demonstrate its versatility and many benefits to a series of signal/image processing and computer vision applications.
- Keywords
- Ghent Analysis & PDE center, Filtering, Smoothing methods, Minimization, Mathematical models, Filtering algorithms, Kernel, Information filters, Semi-sparsity priors, edge-preserving filtering, image smoothing, image enhancement and abstraction, mesh denoising, QUALITY ASSESSMENT, IMAGE, DECOMPOSITION, CONVERGENCE, ALGORITHMS
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01GYVPDC1XPBQYZXBEX6RDB335
- MLA
- Huang, Junqing, et al. “Semi-Sparsity for Smoothing Filters.” IEEE TRANSACTIONS ON IMAGE PROCESSING, vol. 32, 2023, pp. 1627–39, doi:10.1109/TIP.2023.3247181.
- APA
- Huang, J., Wang, H., Wang, X., & Ruzhansky, M. (2023). Semi-sparsity for smoothing filters. IEEE TRANSACTIONS ON IMAGE PROCESSING, 32, 1627–1639. https://doi.org/10.1109/TIP.2023.3247181
- Chicago author-date
- Huang, Junqing, Haihui Wang, Xuechao Wang, and Michael Ruzhansky. 2023. “Semi-Sparsity for Smoothing Filters.” IEEE TRANSACTIONS ON IMAGE PROCESSING 32: 1627–39. https://doi.org/10.1109/TIP.2023.3247181.
- Chicago author-date (all authors)
- Huang, Junqing, Haihui Wang, Xuechao Wang, and Michael Ruzhansky. 2023. “Semi-Sparsity for Smoothing Filters.” IEEE TRANSACTIONS ON IMAGE PROCESSING 32: 1627–1639. doi:10.1109/TIP.2023.3247181.
- Vancouver
- 1.Huang J, Wang H, Wang X, Ruzhansky M. Semi-sparsity for smoothing filters. IEEE TRANSACTIONS ON IMAGE PROCESSING. 2023;32:1627–39.
- IEEE
- [1]J. Huang, H. Wang, X. Wang, and M. Ruzhansky, “Semi-sparsity for smoothing filters,” IEEE TRANSACTIONS ON IMAGE PROCESSING, vol. 32, pp. 1627–1639, 2023.
@article{01GYVPDC1XPBQYZXBEX6RDB335,
abstract = {{In this paper, we propose a semi-sparsity smoothing method based on a new sparsity-induced minimization scheme. The model is derived from the observations that semi-sparsity prior knowledge is universally applicable in situations where sparsity is not fully admitted such as in the polynomial-smoothing surfaces. We illustrate that such priors can be identified into a generalized L-0-norm minimization problem in higher-order gradient domains, giving rise to a new "feature-aware" filter with a powerful simultaneous-fitting ability in both sparse singularities (corners and salient edges) and polynomial-smoothing surfaces. Notice that a direct solver to the proposed model is not available due to the non-convexity and combinatorial nature of L-0-norm minimization. Instead, we propose to solve it approximately based on an efficient half-quadratic splitting technique. We demonstrate its versatility and many benefits to a series of signal/image processing and computer vision applications.}},
author = {{Huang, Junqing and Wang, Haihui and Wang, Xuechao and Ruzhansky, Michael}},
issn = {{1057-7149}},
journal = {{IEEE TRANSACTIONS ON IMAGE PROCESSING}},
keywords = {{Ghent Analysis & PDE center,Filtering,Smoothing methods,Minimization,Mathematical models,Filtering algorithms,Kernel,Information filters,Semi-sparsity priors,edge-preserving filtering,image smoothing,image enhancement and abstraction,mesh denoising,QUALITY ASSESSMENT,IMAGE,DECOMPOSITION,CONVERGENCE,ALGORITHMS}},
language = {{eng}},
pages = {{1627--1639}},
title = {{Semi-sparsity for smoothing filters}},
url = {{http://doi.org/10.1109/TIP.2023.3247181}},
volume = {{32}},
year = {{2023}},
}
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