Parametric Slant-Hadamard transforms with applications

In this letter, we propose a new construction method for a class of parametric Slant-Hadamard transforms that includes but is not limited to the commonly used Slant-Hadamard and Hadamard transforms. This parametric class performs better in generalized Wiener filtering than existing Slant-Hadamard transforms for the first-order Markov and the generalized image correlation models. We also show that the new parametric Slant-Hadamard transform outperforms the commonly used discrete cosine transform for the generalized image correlation model.