@inproceedings{4f78e51785994fe5a1141f18209b30b7,
title = "Two-Dimensional Linear Canonical Stockwell Transform",
abstract = "The linear canonical Stockwell transform (LCST) is an extension of the Stockwell transform (ST) and the linear canonical Fourier transform (LCT). It not only characterizes signals in the time-linear canonical frequency plane but also inherits the advantages of the Stockwell transform. This study aims to generalize LCST into a two-dimensional linear canonical Stockwell transform (2D LCST) in response to the widespread interest in 2D ST across various fields. We begin by examining the fundamental aspects of the two-dimensional linear canonical Stockwell transform, including its definition, basic properties, and Parseval formula. Subsequently, we introduce and investigate a comprehensive reconstruction formula and an energy formula. As we approach the conclusion, we derive the convolution theorem and the cross-correlation theorem associated with the two-dimensional linear canonical Stockwell transform.",
keywords = "2D LCST, Convolution Theorem, Cross-Correlation Theorem, Parseval Formula",
author = "Cao, {Ao Xin} and Li, {Bing Zhao}",
note = "Publisher Copyright: {\textcopyright} 2024 SPIE.; 15th International Conference on Signal Processing Systems, ICSPS 2023 ; Conference date: 17-11-2023 Through 19-11-2023",
year = "2024",
doi = "10.1117/12.3023257",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Zhenkai Zhang and Cheng Li",
booktitle = "Fifteenth International Conference on Signal Processing Systems, ICSPS 2023",
address = "United States",
}