Restoration of Time-Varying Graph Signals using Deep Algorithm Unrolling
Restoration of Time-Varying Graph Signals using Deep Algorithm Unrolling
复制标题
DOI：
10.1109/icassp49357.2023.10094838
复制
发表时间：
2023-06
期刊：
影响因子：
--
通讯作者：
Hayate Kojima;Hikari Noguchi;Koki Yamada;Yuichi Tanaka
中科院分区：
文献类型：
--
作者：
Hayate Kojima;Hikari Noguchi;Koki Yamada;Yuichi Tanaka
在本文中，我们提出了一种时变图信号的恢复方法，即信号值随时间变化的图上的信号，使用深度算法展开。深度算法展开是一种利用深度学习技术在迭代优化算法中学习参数的方法。期望在迭代步骤仍然可解释的情况下提高收敛速度和精度。在该方法中，提出了最小化问题，使时变图信号在时间和空间上都是光滑的。内部参数，即时域FIR滤波器和正则化参数，从训练数据中学习。利用合成数据和实际海温数据进行的实验结果表明，与现有的几种时变图信号重构方法相比，该方法提高了信号重构的精度。
In this paper, we propose a restoration method of time-varying graph signals, i.e., signals on a graph whose signal values change over time, using deep algorithm unrolling. Deep algorithm unrolling is a method that learns parameters in an iterative optimization algorithm with deep learning techniques. It is expected to improve convergence speed and accuracy while the iterative steps are still interpretable. In the proposed method, the minimization problem is formulated so that the time-varying graph signal is smooth both in time and spatial domains. The internal parameters, i.e., time domain FIR filters and regularization parameters, are learned from training data. Experimental results using synthetic data and real sea surface temperature data show that the proposed method improves signal reconstruction accuracy compared to several existing time-varying graph signal re- construction methods.