Low-light image enhancement plays a crucial role in fields such as surveillance, photography, and medical imaging, where inadequate lighting significantly reduces image quality, leading to loss of detail and increased noise. Traditional enhancement methods, such as histogram equalization and Retinex, struggle to preserve fine details and often amplify noise, limiting their effectiveness in real-world applications. To address these issues, this study proposes a Generative Adversarial Networks (GANs)-based model to enhance low-light images in real-time while maintaining high visual fidelity. The model aims to improve contrast, reduce noise, and retain image structure more effectively than conventional methods. The proposed GAN model is trained using the LOL and SID datasets and evaluated using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). Experimental results show that the method achieves a PSNR of 28.4 dB and SSIM of 0.91, outperforming histogram equalization (PSNR: 18.5 dB, SSIM: 0.65) and Retinex (PSNR: 20.3 dB, SSIM: 0.72). Although the model operates in real-time, its inference time of 35.6 ms per image suggests further optimization to support edge computing applications. This study demonstrates that GAN-based enhancement significantly improves low-light images by preserving structural integrity while reducing noise. Future research should focus on optimizing the model for faster processing, experimenting with larger and more diverse datasets, and integrating the system into real-world applications such as automated surveillance and smart camera technologies.
