Breast cancer can be detected by mammograms, but not all of them are of high enough quality to be diagnosed by physicians or radiologists. Therefore, denoising and contrast enhancement in the image are issues that need to be addressed. There are numerous techniques to reduce noise and enhance contrast; the most popular of which incorporate spatial filters and histogram equalization. However, these techniques occasionally result in image blurring, particularly around the edges. The purpose of this article is to propose a technique that uses wavelet denoising in conjunction with top-hat and bottom-hat morphological transforms in the wavelet domain to reduce noise and image quality without distorting the image. Use five wavelet functions to test the proposed method: Haar, Daubechies (db3), Coiflet (coif3), Symlet (sym3), and Biorthogonal (bior1.3); each wavelet function employs levels 1 through 4 with four types of wavelet shrinkage: Bayer, Visu, SURE, and Normal. Three flat structuring elements in the shapes of a disk, a square, and a diamond with sizes 2, 5, 10, 15, 20, and 30 are utilized for top-hat and bottom-hat morphological transforms. To determine optimal parameters, the proposed method is applied to mdb001 mammogram (mini MIAS database) contaminated with Gaussian noise with SD,  = 20. Based on the quality assessment quantities, the Symlet wavelet (sym3) at level 3, with Visu shrinkage and diamond structuring element size 5 produced the best results (MSE = 50.020, PSNR = 31.140, SSIM = 0.407, and SC = 1.008). The results demonstrate the efficacy of the proposed method.