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In the current manuscript, we delineate an Accelerated Condat-Vũ (ACV) algorithm to tackle the complexities associated with the deconvolution problem. In contrast to the traditional Condat-Vũ approach, the ACV framework achieves ideal convergence rates and greatly outperforms the deterministic algorithm, as demonstrated in Driggs in SIAM J Imag Sci 17(4):2076–2109 [1]. Our investigation goes beyond theoretical validation, delving into numerical experiments to illustrate the method’s increased efficiency over its predecessors, highlighting its practical applicability in real-world deconvolution circumstances.

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Total Variation Regularized Sparse Image Deconvolution via Accelerated Condat-Vũ Algorithm

  • 会议论文

  • 发表日期：

    2025年

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  • Yasmine El Mobariki,
  • Amine Laghrib

摘要

In the current manuscript, we delineate an Accelerated Condat-Vũ (ACV) algorithm to tackle the complexities associated with the deconvolution problem. In contrast to the traditional Condat-Vũ approach, the ACV framework achieves ideal convergence rates and greatly outperforms the deterministic algorithm, as demonstrated in Driggs in SIAM J Imag Sci 17(4):2076–2109 [1]. Our investigation goes beyond theoretical validation, delving into numerical experiments to illustrate the method’s increased efficiency over its predecessors, highlighting its practical applicability in real-world deconvolution circumstances.