TY - GEN
T1 - Sampling Based on Joint Time-Vertex Linear Canonical Transform
AU - Zhang, Yu
AU - Li, Bing Zhao
AU - Xin, Hong Cai
N1 - Publisher Copyright:
© 2025 Copyright held by the owner/author(s).
PY - 2025/9/30
Y1 - 2025/9/30
N2 - Joint time-vertex graph signal processing has seen significant advancements recently. This paper investigates the sampling and recovery in the joint time-vertex linear canonical transform (JLCT) domain. We prove that bandlimited signals in the JLCT domain can be perfectly recovered. Experimental design strategies are employed to generate optimal sampling operators on time-vertex graphs. Furthermore, numerical experiments demonstrate superior performance compared to methods based on the joint time-vertex Fourier transform and fractional Fourier transform.
AB - Joint time-vertex graph signal processing has seen significant advancements recently. This paper investigates the sampling and recovery in the joint time-vertex linear canonical transform (JLCT) domain. We prove that bandlimited signals in the JLCT domain can be perfectly recovered. Experimental design strategies are employed to generate optimal sampling operators on time-vertex graphs. Furthermore, numerical experiments demonstrate superior performance compared to methods based on the joint time-vertex Fourier transform and fractional Fourier transform.
KW - graph linear canonical transform
KW - Graph signal processing
KW - sampling.
KW - time-vertex signal processing
UR - https://www.scopus.com/pages/publications/105021309099
U2 - 10.1145/3749859.3749877
DO - 10.1145/3749859.3749877
M3 - Conference contribution
AN - SCOPUS:105021309099
T3 - IVSP 2025 - 2025 7th International Conference on Image, Video and Signal Processing
SP - 135
EP - 139
BT - IVSP 2025 - 2025 7th International Conference on Image, Video and Signal Processing
PB - Association for Computing Machinery, Inc
T2 - 7th International Conference on Image, Video and Signal Processing, IVSP 2025
Y2 - 4 March 2025 through 6 March 2025
ER -