Offset Linear Canonical Stockwell Transform

ABSTRACT In this paper, we investigate a novel convolution structure in the offset linear canonical transform domain to harness the strengths of both the offset linear canonical and Stockwell transforms. This integration results in a new integral transform, the offset linear canonical Stockwell transform (OLCST). We also explore its associated constant Q‐property in the joint time‐frequency domain. Additionally, we present some practical applications. Our preliminary analysis includes deriving fundamental properties, such as Parseval's theorem, the reconstruction formula, and the range theorem. Moreover, we introduce Heisenberg's uncertainty principle, the logarithmic uncertainty principle, the local uncertainty principle, and Nazarov's uncertainty principle in connection with the proposed OLCST.

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Publication Details

Journal
Mathematical Methods in the Applied Sciences
Published
2025-09-23
DOI
https://doi.org/10.1002/mma.70143
Primary Topic
Image and Signal Denoising Methods
Type
article
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article

Offset Linear Canonical Stockwell Transform

Amit K. Verma, Bivek Gupta, Navneet Kaur
Mathematical Methods in the Applied Sciences
Image and Signal Denoising Methods
article

Offset Linear Canonical Stockwell Transform

Amit K. Verma, Bivek Gupta, Navneet Kaur
article en

Abstract

ABSTRACT In this paper, we investigate a novel convolution structure in the offset linear canonical transform domain to harness the strengths of both the offset linear canonical and Stockwell transforms. This integration results in a new integral transform, the offset linear canonical Stockwell transform (OLCST). We also explore its associated constant Q‐property in the joint time‐frequency domain. Additionally, we present some practical applications. Our preliminary analysis includes deriving fundamental properties, such as Parseval's theorem, the reconstruction formula, and the range theorem. Moreover, we introduce Heisenberg's uncertainty principle, the logarithmic uncertainty principle, the local uncertainty principle, and Nazarov's uncertainty principle in connection with the proposed OLCST.

Mathematical Methods in the Applied SciencesVol. 49(1)
Indian Institute of Technology Patna (IN), Indian Institute of Technology Jodhpur (IN), Nirma University (IN)
Openalex Percentile: Top 74%
Image and Signal Denoising Methods
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