Self-Adaptive Alternating Direction Method of Multipliers for Image Denoising
In this study, we introduce a novel self-adaptive alternating direction method of multipliers tailored for image denoising. Our approach begins by formulating a collaborative regularization model that upholds structured sparsity within images while delving into spatial correlations among pixels. To...
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Veröffentlicht in: | Applied sciences 2024-11, Vol.14 (22), p.10427 |
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Hauptverfasser: | , |
Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | In this study, we introduce a novel self-adaptive alternating direction method of multipliers tailored for image denoising. Our approach begins by formulating a collaborative regularization model that upholds structured sparsity within images while delving into spatial correlations among pixels. To address the challenge of penalty parameter influence on convergence speed, we innovate by proposing a self-adaptive alternating direction method of multipliers. This adaptive technique autonomously adjusts variable penalty parameters to expedite algorithm convergence, thereby markedly boosting algorithmic performance. Through a fusion of simulations and empirical analyses, our research demonstrates that this novel methodology significantly amplifies the efficacy of denoising processes. |
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ISSN: | 2076-3417 2076-3417 |
DOI: | 10.3390/app142210427 |