Breast cancer is a highly lethal form of cancer that primarily affects women. Its early detection has been proven to significantly improve the likelihood of survival. Mammography is the primary diagnostic tool used for breast cancer, but in the early stages, it can be challenging for physicians to accurately identify tumors on mammogram images. To tackle this issue, image enhancement techniques are necessary. In this study, we introduce a novel image enhancement method that combines homomorphic filtering and contrast-limited adaptive histogram equalization (CLAHE). The outcomes obtained were compared to those of conventional filters and deep learning methods. The performance of the different methods was assessed using image dissimilarity parameters such as entropy(E), Michelson contrast (MC), mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). The proposed method demonstrates superior image quality when compared to the other methods.