Real-time video enhancement for various weather conditions using dark channel and fuzzy logic

Alajarmeh, Ahmad; Salam, Rosalina Abdul; Marhusin, Mohd Fadzli; Abdulrahim, Khairi

doi:10.1109/iccoins.2014.6868351

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…• Sky foggy image: In general, at the top of the image will be the sky part which occupies the most area of the image, under it, there is the most influenced part of the image. Pixel values in the close (lowermost) part are not the most influenced by fog and vice versa [50], see Figure 4d. In the sky areas of an image, pixels usually have a high intensity in all colour channels [35].…”

Section: Different Fog Typesmentioning

confidence: 90%

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: Different Fog Typesmentioning

confidence: 90%

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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“…These domains include diverse applications such as healthcare [76,77] particularly for elderly people [34], fusion of images (infrared and visible regions) [35], and events determination [40]. Further research contributions in the surveillance domain include static and moving objects in surveillance [44], activity modeling [9], video enhancement [46,78], risk assessment [47], and walk directions estimations [52,79]. As an example, a recent work has employed fuzzy logic for open-set single-sample face recognition in real-world surveillance scenarios [80].…”

Section: Fuzzy Logic Based Methods For Surveillance Bvdmentioning

confidence: 99%

Fuzzy Logic in Surveillance Big Video Data Analysis

et al. 2021

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CCTV cameras installed for continuous surveillance generate enormous amounts of data daily, forging the term Big Video Data (BVD). The active practice of BVD includes intelligent surveillance and activity recognition, among other challenging tasks. To efficiently address these tasks, the computer vision research community has provided monitoring systems, activity recognition methods, and many other computationally complex solutions for the purposeful usage of BVD. Unfortunately, the limited capabilities of these methods, higher computational complexity, and stringent installation requirements hinder their practical implementation in real-world scenarios, which still demand human operators sitting in front of cameras to monitor activities or make actionable decisions based on BVD. The usage of human-like logic, known as fuzzy logic, has been employed emerging for various data science applications such as control systems, image processing, decision making, routing, and advanced safety-critical systems. This is due to its ability to handle various sources of real-world domain and data uncertainties, generating easily adaptable and explainable data-based models. Fuzzy logic can be effectively used for surveillance as a complementary for huge-sized artificial intelligence models and tiresome training procedures. In this article, we draw researchers’ attention toward the usage of fuzzy logic for surveillance in the context of BVD. We carry out a comprehensive literature survey of methods for vision sensory data analytics that resort to fuzzy logic concepts. Our overview highlights the advantages, downsides, and challenges in existing video analysis methods based on fuzzy logic for surveillance applications. We enumerate and discuss the datasets used by these methods, and finally provide an outlook toward future research directions derived from our critical assessment of the efforts invested so far in this exciting field.

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“…In the haze image, intensity of these dark pixels can directly deliver exact estimation of haze's transmission. Various enhancement and dehazing techniques based on dark channel [18], [19], [20], [21], [23] have been developed earlier by researchers to remove the haze and improve the quality of the images. For an arbitrary image J(x), its dark channel is given by equation ( …”

Section: Dark Channel Prior (Dcp)mentioning

confidence: 99%

Performance Evaluation of Fuzzy based DCP and AHE for Underwater Image Haze Removal

Talwar¹,

Kochher²

2015

IJCA

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Underwater images are used to explore the unique life and the world that exists under the water. These images have less clarity, diminishing colors, low contrast .All these issues are a result of haze in underwater images. So, Underwater image haze removal algorithms become very important and necessary for many vision applications. In this paper, after a brief overview of existing methods of haze removal and discovering their limitations, we present a novel technique of underwater image haze removal based on Fuzzy Based DCP and AHE. AHE has been used to remove the problem of uneven illumination. The proposed technique has the ability to remove the limitations of existing techniques. Different kind of image quality assessment metrics have been used to evaluate the effectiveness of proposed technique over the existing one. The proposed technique give efficient results for different hazy underwater images by removing the haze to a good extent and improving the quality.

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Real-time video enhancement for various weather conditions using dark channel and fuzzy logic

Cited by 6 publications

References 13 publications

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

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

Fuzzy Logic in Surveillance Big Video Data Analysis

Performance Evaluation of Fuzzy based DCP and AHE for Underwater Image Haze Removal

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