Comprehensive Review of Machine Learning (ML) in Image Defogging: Taxonomy of Concepts, Scenes, Feature Extraction, and Classification techniques

Zainab Hussein Arif, Moamin A. Mahmoud, Karrar Hameed Abdulkareem, Mazin Abed Mohammed, Mohammed Nasser Al-Mhiqani, Ammar Awad Mutlag, Robertas Damaševičius

Research output: Contribution to journalReview articlepeer-review

17 Citations (Scopus)


Images captured through a visual sensory system are degraded in a foggy scene, which negatively influences recognition, tracking, and detection of targets. Efficient tools are needed to detect, pre-process, and enhance foggy scenes. Machine learning (ML) has a significant role in image defogging domain for tackling adverse issues. Unfortunately, regardless of 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 defogging research area. Also, the basic terms and concepts are highlighted in image defogging topic. Feature extraction approaches with a summary of advantages and disadvantages are described. ML algorithms are also summarized that have been used for applications related to image defogging, that is, image denoising, image quality assessment, image segmentation, and foggy image classification. Open datasets are also discussed. Finally, the existing problems of the image defogging domain in general and, specifically related to ML which need to be further studied are discussed. To the best knowledge, this the first review paper which sheds a light on the role of ML and relevant aspects in the image defogging domain.

Original languageEnglish
Pages (from-to)289-310
Number of pages22
JournalIET Image Processing
Issue number2
Early online date23 Nov 2021
Publication statusPublished - 1 Feb 2022
Externally publishedYes

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