论文标题
重新访问1D总变化恢复问题，以及新的实时算法，用于信号和超参数估计
Revisit 1D Total Variation restoration problem with new real-time algorithms for signal and hyper-parameter estimations
论文作者
论文摘要
考虑到数据保真度和总变化（TV）正则化，一维总变化（TV）降级，提出了一个良好的恢复信号保留形状边缘。主要问题是如何选择平衡这两个术语的权重$λ$。实际上，通过评估候选人列表（例如交叉验证）来选择此参数，这对于实时应用程序不合适。在这项工作中，我们重新访问Tibshirani和Taylor提出的1D总变异恢复算法。集成了一种启发式方法，用于根据恢复信号的极值估算$λ$的良好选择。我们在O（n log N）中提出了一个离线版本的恢复算法，以及其在O（n）中的在线实现。结合了快速算法和$λ$的自动选择，我们提出了一种实时自动Denoising算法，提供了一个较大的应用程序字段。模拟表明，我们对$λ$的主张的性能与艺术状态相似。
1D Total Variation (TV) denoising, considering the data fidelity and the Total Variation (TV) regularization, proposes a good restored signal preserving shape edges. The main issue is how to choose the weight $λ$ balancing those two terms. In practice, this parameter is selected by assessing a list of candidates (e.g. cross validation), which is inappropriate for the real time application. In this work, we revisit 1D Total Variation restoration algorithm proposed by Tibshirani and Taylor. A heuristic method is integrated for estimating a good choice of $λ$ based on the extremums number of restored signal. We propose an offline version of restoration algorithm in O(n log n) as well as its online implementation in O(n). Combining the rapid algorithm and the automatic choice of $λ$, we propose a real-time automatic denoising algorithm, providing a large application fields. The simulations show that our proposition of $λ$ has a similar performance as the states of the art.
