Single Image Haze Removal Techniques Based on Priors, Fusion and Deep Learning: A Comprehensive Survey

Abstract Image haze removal has a significant involvement in numerous computer vision (CV) related uses. The primary purpose of the present article is to outline the current deep learning (based on CNN) processes used for single image dehazing. The previous problems with the haze removal methods based on multiple images are first addressed. Then, fundamental concepts of atmospheric scattering model and Convolutional Neural Network (CNN) are explained. The currently available single image dehazing approaches are divided into 3 groups: prior based, image fusion based and deep learning based approaches. Highlights and challenges of these dehazing techniques are discussed. The synthetic and real datasets utilized by different researchers in dehazing techniques are described along with implementation details. The paper also mentions performance metrics for evaluating image quality.