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Enhancement of mammogram images against low-contrast and poor illumination is still a challenge for researchers. Focusing on such issues, this manuscript presents a two-stage enhancement technique for mammogram images. The first stage of the image enhancement deals with illumination control using the corrective-adaptive gamma correction (CAGC) approach. In the second stage, contrast enhancement operation on the luminosity-controlled image is incorporated. In order to enhance visual perception against low-contrast, the combined application of discrete wavelet transform (DWT) and singular value decomposition (SVD) is incorporated. The experimentation of the proposed technique was performed over a publicly available mini-MIAS dataset. The proposed technique is evaluated on various quantitative parameters such as Pearson correlation coefficient (PCC), universal image quality index (IQI), structural similarity index measurement (SSIM), contrast improvement index (CII), average mean brightness error (AMBE), and mean absolute error (MAE) and obtain the average values of 0.996, 0.912, 0.921, 1.098, 15.732, and 15.624 that are promising results as compared to the other traditional methods. This study also compares the proposed technique with state-of-art methods and achieves better performance, resulting in significant improvement in contrast enhancement and local information preservation of mammogram images.

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