In this chapter, we detail our image denoisingImage denoising approach proposed in literature. It consists firstly of applying the 2D dual-tree DWT2D dual-tree DWT to the noisy image. Secondly, the noisy wavelet coefficients obtained in the first step, are denoised by applying to each of them, a SWT-based image denoising technique. Finally, the denoised image is reconstructed by applying the inverse of the 2D dual-tree DWT2D dual-tree DWT to the denoised wavelet coefficients obtained in the second step. For applying this SWT-based image denoisingImage denoising technique, we employ the soft thresholdingThresholding, the Daubechies 4 as the mother wavelet, and the decomposition level is equal to 5. The performance of this approach was proven by its comparison to three other denoising techniquesDenoising techniques existing in the literature. These three techniques are the denoising method based on the soft thresholding in the SWT domain, the image denoisingImage denoising technique based on soft thresholdingThresholding in the domain of 2D dual-tree DWT2D dual-tree DWT and the image denoising approach using deep neural network. All the previously mentioned techniques, including our proposed denoising approach, are applied to a number of noisy images, and the obtained results are in terms of peak signal-to-noise ratio (PSNR)Peak Signal-to-Noise Ratio (PSNR) and structural similarity (SSIM)Structural Similarity (SSIM). These results show that this proposed denoising technique outperforms the other three denoising techniquesDenoising techniques applied for this evaluation.