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.
Authors
- Amit K. Verma (ORCID: https://orcid.org/0000-0001-8768-094X)
- Bivek Gupta (ORCID: https://orcid.org/0000-0003-2269-6427)
- Navneet Kaur
Institutions
- Indian Institute of Technology Patna (IN)
- Indian Institute of Technology Jodhpur (IN)
- Nirma University (IN)
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
- Field-Weighted Citation Impact
- 0.00