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|a 10.1109/TIP.2023.3247181
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|a eng
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|a Huang, Junqing
|e verfasserin
|4 aut
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| 245 |
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|a Semi-Sparsity for Smoothing Filters
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|c 2023
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 04.04.2025
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a 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
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|a Journal Article
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| 700 |
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|a Wang, Haihui
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Xuechao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ruzhansky, Michael
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 32(2023) vom: 04., Seite 1627-1639
|w (DE-627)NLM09821456X
|x 1941-0042
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|g volume:32
|g year:2023
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|g pages:1627-1639
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|u http://dx.doi.org/10.1109/TIP.2023.3247181
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