AI-Driven Low-Light Image Enhancement Using Deep Neural Networks

Low-light image degradation remains a significant challenge in computer vision applications, affecting visibility, feature extraction, and overall visual quality. Images captured under insufficient illumination often suffer from noise, low contrast, color distortion, and loss of detail, which can negatively impact downstream tasks such as object detection, surveillance, autonomous navigation, and medical imaging. This research presents an AI-driven low-light image enhancement framework using deep neural networks (DNNs) to improve image clarity and visibility under poor lighting conditions. The proposed approach leverages convolutional neural networks (CNNs) to learn complex illumination patterns and restore brightness, contrast, and color balance while suppressing noise. The model is trained on paired and unpaired low-light datasets using a combination of perceptual loss, reconstruction loss, and adversarial learning techniques to ensure natural-looking enhancement. Unlike traditional histogram equalization or gamma correction methods, the deep learning model adaptively enhances images without overexposure or information loss. Experimental evaluation demonstrates significant improvements in Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and visual perceptual quality compared to conventional enhancement techniques. The proposed system achieves robust performance across diverse low-light scenarios, making it suitable for real-time applications in surveillance systems, automotive night vision, robotics, and mobile photography. This study highlights the effectiveness of deep neural networks in addressing illumination challenges and advancing intelligent image enhancement technologies.