Mammograms are crucial in breast cancer detection, and enhancing their quality can lead to improved diagnostic accuracy. The Non-Local Means (NLM) filter, by considering the similarities between image patches, effectively reduces noise while preserving important details, such as microcalcifications, masses, and other subtle structures that might be indicative of abnormalities. Unsharp masking involves creating a high-pass version of the original image and then adding it back with the original to enhance edges and details. An NLM filter is integrated within an unsharp masking framework, accompanied by linear arithmetic operations that allows for further refinement of the enhancement process, resulting in mammogram images that display improved contrast, edge sharpness, and overall quality. These findings prominently demonstrate the potential of our approach to enhance image quality, achieve noise reduction, and refine sharpness, reinforcing its significance in tasks involving image enhancement, noise removal, and sharpening.