论文标题
基于白度的参数选择，用于变异图像处理中的泊松数据
Whiteness-based parameter selection for Poisson data in variational image processing
论文作者
论文摘要
我们提出了一种新型的自动参数选择策略，以解决泊松噪声腐败下的变分成像问题。在低光子计数制度中，选择合适的正则化参数，其价值对于实现高质量重建至关重要，其价值至关重要。在这项工作中，我们扩展了最初为载液噪声设计的所谓残留白度原理。提出的策略依赖于标准化泊松噪声过程的白度特性的研究。在得出激励我们的建议的理论属性之后，我们使用乘数的交替方向方法的线性化版本解决了目标最小化问题，该方法在存在一般线性正向运算符的情况下特别适合。我们的策略在图像恢复和计算机断层扫描重建问题上进行了广泛的测试，并将其与Zanella在Al提出的Poisson噪声的众所周知的差异原理进行了比较。并带有作者先前提出的几乎精确版本。
We propose a novel automatic parameter selection strategy for variational imaging problems under Poisson noise corruption. The selection of a suitable regularization parameter, whose value is crucial in order to achieve high quality reconstructions, is known to be a particularly hard task in low photon-count regimes. In this work, we extend the so-called residual whiteness principle originally designed for additive white noise to Poisson data. The proposed strategy relies on the study of the whiteness property of a standardized Poisson noise process. After deriving the theoretical properties that motivate our proposal, we solve the target minimization problem with a linearized version of the alternating direction method of multipliers, which is particularly suitable in presence of a general linear forward operator. Our strategy is extensively tested on image restoration and computed tomography reconstruction problems, and compared to the well-known discrepancy principle for Poisson noise proposed by Zanella at al. and with a nearly exact version of it previously proposed by the authors.
