Optimizing Contrast in Low-Light Imaging Machine Learning-Based Techniques for Weak Contrast Adjustment and Enhancement in Visual Data

Low-light imaging suffers from reduced contrast, leading to poor visual perception and information loss. Traditional contrast enhancement techniques often introduce artifacts or fail to generalize across diverse environments. Machine learning (ML)-based approaches provide adaptive solutions by learning patterns from vast datasets, enabling robust enhancement in low-light conditions. This paper explores ML techniques such as Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and Reinforcement Learning (RL) for contrast enhancement. We evaluate these models on benchmark datasets using metrics like Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Mean Squared Error (MSE). Experimental results demonstrate that ML models outperform conventional methods, achieving significant improvements in contrast and clarity. Future research should focus on real-time deployment and optimization for resource-constrained devices.