Low-light photographs are less effective for surveillance, medical imaging, and autonomous driving systems due to their reduced contrast, noise, and poor visibility. The objective of this project is to enhance the quality of photographs that are poorly lit by utilizing both local and global enhancement techniques simultaneously. The primary goal is to preserve natural characteristics while improving visual clarity, contrast, and luminosity. The proposed method utilizes noise reduction techniques, adaptive histogram equalization, gamma correction, and contrast stretching to improve the appearance of photographs that were taken in low-light conditions. PSNR and SSIM are implemented in experiments to evaluate the quality of structural protection and augmentation. In comparison to conventional methods, the proposed method significantly improves contrast, visibility, and detail retention. This research improves the efficiency of computer vision systems that operate in low-light environments and establishes a valuable framework for future advancements that facilitate the efficient operation of real-time image processing applications.