Contrast is an imperative perceptible attribute embodying the image quality. In medical images, the poor quality specifically low contrast inhibits precise interpretation of the image. Contrast enhancement is, therefore, applied not merely to improve the visual quality of images but also enabling them to facilitate further processing tasks. We propose a contrast enhancement approach based on cross-modal learning in this paper. Cycle-GAN (Generative Adversarial Network) is used for this purpose, where UNet augmented with global features acts as a generator. Besides, individual batch normalization has been used to make generators adapt specifically to their input distributions. The proposed method accepts low contrast T2-weighted (T2-w) Magnetic Resonance images (MRI) and uses the corresponding high contrast T1-w MRI to learn the global contrast characteristics. The experiments were conducted on a publicly available IXI dataset. Comparison with recent CE methods and quantitative assessment using two prevalent metrics FSIM and BRISQUE validate the superior performance of the proposed method.