The noise emergence in the digital image can occur throughout image acquisition, transmission, and processing steps. Consequently, eliminating the noise from the digital image is required before further processing. This study details the denoising of noisy images (including magnetic resonance images (MRIs)) by employing our image denoising approach proposed in literature. This proposed approach is based on the stationary wavelet transform (SWT 2D)SWT 2D and the 2D dual-tree discrete wavelet transform (DWT). The first step of this approach consists of applying the 2D dual-tree DWT to the noisy image to obtain noisy wavelet coefficients. The second step of this approach consists of denoising each of these coefficients by applying an SWT 2D-based denoising technique. The denoised image is finally obtained by applying the inverse of the 2D dual-tree DWT to the denoised coefficients obtained in the second step. This approach was evaluated by comparing it to four denoising techniquesDenoising techniques existing in literature. The latters are the image denoisingImage denoising technique based on thresholdingThresholding in the SWT-2DSWT-2D domain, the image denoising technique based on deep neural network, the image denoisingImage denoising technique based on soft thresholding in the domain of 2D dual-tree DWT, and non-local means filter. This proposed approach, and the other four techniques formerly mentioned, were applied to a number of noisy grey-scale images and also noisy magnetic resonance images (MRIs) and the obtained results were in terms of PSNRPeak Signal-to-Noise Ratio (PSNR) (peak signal-to-noise ratio), SSIMStructural Similarity (SSIM) (structural similarity), normalized mean square error (NMSE) and feature similarity (FSIM)Feature Similarity (FSIM). These results show that this proposed approach outperforms the other four denoising techniquesDenoising techniques mentioned previously.