4 research outputs found
Spectral Domain Spline Graph Filter Bank
In this paper, we present a structure for two-channel spline graph filter
bank with spectral sampling (SGFBSS) on arbitrary undirected graphs. Our
proposed structure has many desirable properties; namely, perfect
reconstruction, critical sampling in spectral domain, flexibility in choice of
shape and cut-off frequency of the filters, and low complexity implementation
of the synthesis section, thanks to our closed-form derivation of the synthesis
filter and its sparse structure. These properties play a pivotal role in
multi-scale transforms of graph signals. Additionally, this framework can use
both normalized and non-normalized Laplacian of any undirected graph. We
evaluate the performance of our proposed SGFBSS structure in nonlinear
approximation and denoising applications through simulations. We also compare
our method with the existing graph filter bank structures and show its superior
performance.Comment: 5 pages, 6 figures, and one tabl
Graph Signal Processing: A Signal Representation Approach to Convolution and Sampling
The paper presents sampling in GSP as 1) linear operations (change of bases)
between signal representations and 2) downsampling as linear shift invariant
filtering and reconstruction (interpolation) as filtering, both in the spectral
domain. To achieve this, it considers a spectral shift that leads to a
spectral graph signal processing theory, , dual to
GSP but that starts from the spectral domain and . The paper introduces
alternative signal representations, convolution of graph signals for these
alternative representations, presenting a GSP convolution that
uses the DSP FFT algorithm, and sampling as solutions of algebraic linear
systems of equations.Comment: Added missing space in arXiv titl
Graph Signal Processing: Overview, Challenges and Applications
Research in Graph Signal Processing (GSP) aims to develop tools for
processing data defined on irregular graph domains. In this paper we first
provide an overview of core ideas in GSP and their connection to conventional
digital signal processing. We then summarize recent developments in developing
basic GSP tools, including methods for sampling, filtering or graph learning.
Next, we review progress in several application areas using GSP, including
processing and analysis of sensor network data, biological data, and
applications to image processing and machine learning. We finish by providing a
brief historical perspective to highlight how concepts recently developed in
GSP build on top of prior research in other areas.Comment: To appear, Proceedings of the IEE