Enhancing lung images is essential for accurate medical diagnosis and treatment planning. Traditional image enhancement techniques often struggle to balance contrast improvement and noise suppression. In this paper, a novel method named Dynamic Dual-Histogram Gamma Correction (DDHGC), is presented that combines the ideas of Dualistic Sub-Image Histogram Equalization (DSIHE) and Dynamic Gamma Correction with Weighting Distribution. Furthermore, an Enhanced Selective Median Filter is adopted for noise suppression purpose before executing DDHGC. It offers excellent noise suppression and optimal contrast enhancement capabilities which makes it well suited for medical imaging applications. The aim of this study is to investigate the utility of DDHGC for improving contrast in two different types of medical imaging data - chest X-ray images and lung CT images from publicly available datasets. The results show that the proposed DDHGC method consistently perform better than four common methods (CLAHE, Gamma Correction, AGCWD and DSIHE). It provides the best results for PSNR, SSIM, Entropy and CII outperforming in terms of image quality, structure preservation and enhanced details indicating improved contrast.