提出可调强度的正则化方法，提升图像重建稳定性与收敛性
Enhanced Denoising and Convergent Regularisation Using Tweedie Scaling
- 引入泰迪缩放参数，动态调节正则化强度
- 保证正则化族在迭代中稳定收敛，避免发散问题
- 适合需要可靠收敛性的图像重建研究者使用
图像重建因物理采集限制具有固有的不适定性，通常通过引入结合先验知识的正则化项来解决。针对此类反问题设计的插件式（Plug-and-Play）迭代框架，通过用通用去噪器替代传统正则化项，实现当前最优性能，该去噪器可由神经网络参数化。然而，这类深度学习方法存在关键缺陷：缺乏控制正则化强度的调节参数，导致难以设计收敛的正则化策略。为此，本文提出一种新型缩放方法，显式集成并调节正则化强度。缩放参数不仅提升了去噪器学习过程的可解释性，还系统性改善了优化效果。此外，所提方法确保生成的正则化族在理论上具备稳定性和收敛性。
原文摘要 · Abstract (English)
The inherent ill-posed nature of image reconstruction problems, due to limitations in the physical acquisition process, is typically addressed by introducing a regularisation term that incorporates prior knowledge about the underlying image. The iterative framework of Plug-and-Play methods, specifically designed for tackling such inverse problems, achieves state-of-the-art performance by replacing the regularisation with a generic denoiser, which may be parametrised by a neural network architecture. However, these deep learning approaches suffer from a critical limitation: the absence of a control parameter to modulate the regularisation strength, which complicates the design of a convergent regularisation. To address this issue, this work introduces a novel scaling method that explicitly integrates and adjusts the strength of regularisation. The scaling parameter enhances interpretability by reflecting the quality of the denoiser's learning process, and also systematically improves its optimisation. Furthermore, the proposed approach ensures that the resulting family of regularisations is provably stable and convergent.
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