Images captured through a visual sensory system are degraded in a foggy scene, which neg-atively influences recognition, tracking, and detection of targets. Efficient tools are neededto detect, pre-process, and enhance foggy scenes. Machine learning (ML) has a signifi-cant role in image defogging domain for tackling adverse issues. Unfortunately, regardlessof contributions that were made by ML, little attention has been attributed to this topic.This paper summarizes the role of ML methods and relevant aspects in the image defog-ging research area. Also, the basic terms and concepts are highlighted in image defoggingtopic. Feature extraction approaches with a summary of advantages and disadvantages aredescribed. ML algorithms are also summarized that have been used for applications relatedto image defogging, that is, image denoising, image quality assessment, image segmenta-tion, and foggy image classification. Open datasets are also discussed. Finally, the existingproblems of the image defogging domain in general and, specifically related to ML whichneed to be further studied are discussed. To the best knowledge, this the first review paperwhich sheds a light on the role of ML and relevant aspects in the image defogging domain.
