2 citations · 2 across the 1 of their papers we have counts for
6 papers · 1 filter
Graph Blind Deconvolution with Sparseness Constraint
Kazuma Iwata, Koki Yamada, Yuichi Tanaka
We propose a blind deconvolution method for signals on graphs, with the exact sparseness constraint for the original signal. Graph blind deconvolution is an algorithm for estimatin…
Sampling Signals on Graphs: From Theory to Applications
Yuichi Tanaka, Yonina C. Eldar, Antonio Ortega +1
The study of sampling signals on graphs, with the goal of building an analog of sampling for standard signals in the time and spatial domains, has attracted considerable attention…
Generalized Sampling on Graphs With Subspace and Smoothness Priors
Yuichi Tanaka, Yonina C. Eldar
We propose a framework for generalized sampling of graph signals that parallels sampling in shift-invariant (SI) subspaces. This framework allows for arbitrary input signals, which…
M-Channel Critically Sampled Spectral Graph Filter Banks With Symmetric Structure
Akie Sakiyama, Kana Watanabe, Yuichi Tanaka
This paper proposes a class of -channel spectral graph filter banks with a symmetric structure, that is, the transform has sampling operations and spectral graph filters on both…
Eigendecomposition-Free Sampling Set Selection for Graph Signals
Akie Sakiyama, Yuichi Tanaka, Toshihisa Tanaka +1
This paper addresses the problem of selecting an optimal sampling set for signals on graphs. The proposed sampling set selection (SSS) is based on a localization operator that can…
Two-Channel Critically-Sampled Graph Filter Banks With Spectral Domain Sampling
Akie Sakiyama, Kana Watanabe, Yuichi Tanaka +1
We propose two-channel critically-sampled filter banks for signals on undirected graphs that utilize spectral domain sampling. Unlike conventional approaches based on vertex domain…