Discrete Gyrator Transforms: Computational Algorithms and Applications

TL;DR

This paper introduces discrete gyrator transforms based on the 2D linear canonical transform, providing multiple algorithms with different properties, including an orthonormal version, and explores their applications in optics, signal, and image processing.

Contribution

It develops new discrete gyrator transform algorithms based on the 2D LCT, including an orthonormal transform with reduced complexity and broad application potential.

Findings

Three types of DGTs with different properties are proposed.

An orthonormal DGT based on eigenfunctions is developed.

Efficient algorithms significantly reduce computational complexity.

Abstract

As an extension of the 2D fractional Fourier transform (FRFT) and a special case of the 2D linear canonical transform (LCT), the gyrator transform was introduced to produce rotations in twisted space/spatial-frequency planes. It is a useful tool in optics, signal processing and image processing. In this paper, we develop discrete gyrator transforms (DGTs) based on the 2D LCT. Taking the advantage of the additivity property of the 2D LCT, we propose three kinds of DGTs, each of which is a cascade of low-complexity operators. These DGTs have different constraints, characteristics, and properties, and are realized by different computational algorithms. Besides, we propose a kind of DGT based on the eigenfunctions of the gyrator transform. This DGT is an orthonormal transform, and thus its comprehensive properties, especially the additivity property, make it more useful in many applications. We also develop an efficient computational algorithm to significantly reduce the complexity of this DGT. At the end, a brief review of some important applications of the DGTs is presented, including mode conversion, sampling and reconstruction, watermarking, and image encryption.