Tensor-guided learning for image denoising using anisotropic PDEs

In this article, we introduce an advanced approach for enhanced image denoising using an improved space-variant anisotropic Partial Differential Equation (PDE) framework. Leveraging Weickert-type operators, this method relies on two critical parameters: λ and θ , defining local image geometry and sm...

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Veröffentlicht in:Machine vision and applications 2024-05, Vol.35 (3), p.48, Article 48
Hauptverfasser: Limami, Fakhr-eddine, Hadri, Aissam, Afraites, Lekbir, Laghrib, Amine
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Sprache:eng
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Zusammenfassung:In this article, we introduce an advanced approach for enhanced image denoising using an improved space-variant anisotropic Partial Differential Equation (PDE) framework. Leveraging Weickert-type operators, this method relies on two critical parameters: λ and θ , defining local image geometry and smoothing strength. We propose an automatic parameter estimation technique rooted in PDE-constrained optimization, incorporating supplementary information from the original clean image. By combining these components, our approach achieves superior image denoising, pushing the boundaries of image enhancement methods. We employed a modified Alternating Direction Method of Multipliers (ADMM) procedure for numerical optimization, demonstrating its efficacy through thorough assessments and affirming its superior performance compared to alternative denoising methods.
ISSN:0932-8092
1432-1769
DOI:10.1007/s00138-024-01532-4