activity
20172022
most citedSampling Signals on Graphs: From Theory to Applications

152 citations · 502 across the 13 of their papers we have counts for

collaborators

14 papers

eess.SP2022

Multi-channel Sampling on Graphs and Its Relationship to Graph Filter Banks

Junya Hara, Yuichi Tanaka

In this paper, we consider multi-channel sampling (MCS) for graph signals. We generally encounter full-band graph signals beyond the bandlimited one in many applications, such as p…

eess.SP2022

Regularity-Constrained Fast Sine Transforms

Taizo Suzuki, Seisuke Kyochi, Yuichi Tanaka

This letter proposes a fast implementation of the regularity-constrained discrete sine transform (R-DST). The original DST \textit{leaks} the lowest frequency (DC: direct current)…

eess.SP2022★ 27 cited

Graph Signal Sampling Under Stochastic Priors

Junya Hara, Yuichi Tanaka, Yonina C. Eldar

We propose a generalized sampling framework for stochastic graph signals. Stochastic graph signals are characterized by graph wide sense stationarity (GWSS) which is an extension o…

eess.IV2021

Directional Analytic Discrete Cosine Frames

Seisuke Kyochi, Taizo Suzuki, Yuichi Tanaka

Block frames called directional analytic discrete cosine frames (DADCFs) are proposed for sparse image representation. In contrast to conventional overlapped frames, the proposed D…

eess.SP2020★ 2 cited

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…

eess.SP2020★ 152 cited

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…