A Review on Different Image De-hazing Methods

Shaw, Sweta; Gupta, Rajarshi; Roy, Somshubhra

doi:10.1007/978-981-13-7403-6_46

Cited by 2 publications

(1 citation statement)

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“…Also, it has the main role in different accidents over the land environment [1]. The image which is captured by the camera suffers from degradation, also it has low contrast and darkens visibility [2]. This will completely impact the visual system, particularly the visible light visual system.…”

Section: Introductionmentioning

confidence: 99%

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

Arif

Mahmoud

Abdulkareem

et al. 2021

IET Image Processing

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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.

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Section: Introductionmentioning

confidence: 99%