Master's defence | John Ladan, An Analysis of Stockwell Transforms with Applications to Signal/Image Processing

Time-frequency analysis is a powerful tool for signal analysis and processing. The Fourier transform and wavelet transforms are used extensively as is the Short-Time Fourier Transform (or Gabor transform). In 1996 the Stockwell transform was introduced to maintain the phase of the Fourier transform, hile also providing the progressive resolution of the wavelet transform. The discrete orthonormal Stockwell transform is a more efficient, less redundant transform with the same properties.

 

There has been little work on mathematical properties of the Stockwell transform, particularly how it behaves under operations such as translation and modulation. Previous results do discuss a resolution of the identity, as well as some of the function spaces that may be associate with it. We extend the resolution of the identity results, and behaviour under translation, modulation, rotation, convolution and differentiation. We will also apply the Stockwell transform to various continuous functions.

 

The discrete orthonormal Stockwell transform is compared directly with Newland's harmonic wavelet transform, and we extend the definition to include variations, as well as develop the discrete cosine based Stockwell transform. There has been some work on image processing using the Stockwell transform and discrete orthonormal Stockwell transform. The tests were quite preliminary.  We test all of these discrete transforms against current methods for image compression.