Image Dehazing Using Machine Learning Algoithms

Image dehazing is a crucial preprocessing step in computer vision applications, as haze and fog significantly degrade image quality and reduce visibility. This project explores the use of machine learning algorithms to enhance image clarity by removing haze effects. Various supervised and unsupervised learning techniques are applied to improve contrast, restore lost details, and optimize computational efficiency. The proposed system leverages convolutional neural networks (CNNs) and regression-based models to predict haze density and reconstruct clear images. Experimental results demonstrate improved performance compared to traditional dehazing methods. The project contributes to real-world applications such as autonomous driving, surveillance, and remote sensing. (Times New Roman, Size 10, ≤250 words, Justified).