Two-dimensional linear canonical Stockwell transform

Ao-Xin Cao; Bing-Zhao Li

doi:10.1117/12.3023257

28 March 2024 Two-dimensional linear canonical Stockwell transform

Ao-Xin Cao, Bing-Zhao Li

Proceedings Volume 13091, Fifteenth International Conference on Signal Processing Systems (ICSPS 2023); 1309109 (2024) https://doi.org/10.1117/12.3023257
Event: Fifteenth International Conference on Signal Processing Systems (ICSPS 2023), 2023, Xi’an, China

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ao-Xin Cao and Bing-Zhao Li "Two-dimensional linear canonical Stockwell transform", Proc. SPIE 13091, Fifteenth International Conference on Signal Processing Systems (ICSPS 2023), 1309109 (28 March 2024); https://doi.org/10.1117/12.3023257

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