Unsupervised Dehazing Methods: A Review
Subhash Chand Agrawal, Rajesh Kumar Tripathi · 2026
Restoring damaged images from poor weather conditions, particularly unfavorable atmospheric effects that make it harder to see in outdoor images, is one of the toughest image processing tasks. Haze presents problems in many computer vision applications, highlighting the necessity of improving contrast and restoring view in blurry images. An extensive review of convolutional neural network (CNN)-based unsupervised dehazing techniques is presented in this paper. The technique of image dehazing, which enhances the contrast and visibility of hazy images, has attracted a lot of interest from researchers. The survey begins by going over the physical model, datasets, problems and difficulties, typical types of dehazing techniques, and evaluation metrics that are frequently employed in dehazing studies. These terms are crucial for comprehending the fundamental ideas and evaluating dehazing effectiveness. This paper then offers an analytical viewpoint on different deep learning methods for image dehazing. The primary goal is to provide an intuitive grasp of the fundamental methods that have significantly advanced the removal of haze. The study uses a variety of baseline techniques in both quantitative and qualitative tests to set baselines and assess the effectiveness of these algorithms. The authors offer guidance for additional research and advancements in dehazing methods by pointing up these shortcomings.