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<p>This research tackles the task of denoising images corrupted by a combination of noise types using a novel nonlinear partial differential equation (PDE) framework. Our model incorporates both gray level intensity and edge detection functions to dynamically adjust the diffusion rate for individual pixels, optimizing denoising performance. To overcome the challenge of parameter selection, we opt for the Split Bregman algorithm as a numerical implementation. Throughout the paper, we rigorously establish the existence and uniqueness of the model’s solution and showcase its effectiveness through diverse numerical experiments.</p>

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A spatially variable coupled PDE-based model for image denoising using a split Bregman algorithm

  • 期刊论文

  • 发表日期：

    2025年6月23日

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  • A. Mohssine,
  • L. Afraites,
  • A. Laghrib,
  • A. Hadri

摘要

This research tackles the task of denoising images corrupted by a combination of noise types using a novel nonlinear partial differential equation (PDE) framework. Our model incorporates both gray level intensity and edge detection functions to dynamically adjust the diffusion rate for individual pixels, optimizing denoising performance. To overcome the challenge of parameter selection, we opt for the Split Bregman algorithm as a numerical implementation. Throughout the paper, we rigorously establish the existence and uniqueness of the model’s solution and showcase its effectiveness through diverse numerical experiments.