**5. Experimental Results**

We demonstrate the effectiveness of our proposed data-driven redundant transform based on Parseval frames (DRTPF) by first analyzing the robustness of the model against Gaussian White Noise. Then we discuss the convergence of the proposed transform learning algorithm and the ability of the proposed DRTPF to provide low sparsification errors. Finally, we evaluate the effectiveness of the proposed DRTPF by applying it to nature image denoising. We use a fixed step size in the transform update and denoising steps of our algorithms.
