Comprehensive Review of Machine Learning (ML) in Image Defogging: Taxonomy of Concepts, Scenes, Feature Extraction, and Classification techniques. Issue 2 (23rd November 2021)

• Record Type: Journal Article
• Authors: Arif, Zainab Hussein; Mahmoud, Moamin A.; Abdulkareem, Karrar Hameed; Mohammed, Mazin Abed; Al‐Mhiqani, Mohammed Nasser; Mutlag, Ammar Awad; Damaševičius, Robertas
• Is Part Of: IET image processing. Volume 16:Issue 2(2022)
• Page Start: 289
• Page End: 310
• Publication Date: 2021-11-23
• Subjects: Optical, image and video signal processing -- Image recognition -- Image sensors -- Other topics in statistics -- Computer vision and image processing techniques; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/ipr2.12365
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract: 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.