Low and non-uniform illumination color image enhancement using weighted guided image filtering

Mu, Qi; Wang, Xinyue; Wei, Yanfei; Li, Zhanli

doi:10.1007/s41095-021-0232-x

Cited by 14 publications

(4 citation statements)

References 51 publications

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“…Insufficient or non-uniform RGB images contain a significant amount of noise. Therefore, the authors enhance the RGB image based on Mu et al (2021) . Equation (2) shows the intensity enhancement of RGB(x,y).…”

Section: Methodsmentioning

confidence: 99%

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Artificial intelligence-driven malware detection framework for internet of things environment

Alsubai

Dutta²,

Alnajim

et al. 2023

PeerJ Computer Science

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The Internet of Things (IoT) environment demands a malware detection (MD) framework for protecting sensitive data from unauthorized access. The study intends to develop an image-based MD framework. The authors apply image conversion and enhancement techniques to convert malware binaries into RGB images. You only look once (Yolo V7) is employed for extracting the key features from the malware images. Harris Hawks optimization is used to optimize the DenseNet161 model to classify images into malware and benign. IoT malware and Virusshare datasets are utilized to evaluate the proposed framework’s performance. The outcome reveals that the proposed framework outperforms the current MD framework. The framework generates the outcome at an accuracy and F1-score of 98.65 and 98.5 and 97.3 and 96.63 for IoT malware and Virusshare datasets, respectively. In addition, it achieves an area under the receiver operating characteristics and the precision-recall curve of 0.98 and 0.85 and 0.97 and 0.84 for IoT malware and Virusshare datasets, accordingly. The study’s outcome reveals that the proposed framework can be deployed in the IoT environment to protect the resources.

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Section: Methodsmentioning

confidence: 99%

“…The Internet of Things (IoT) connects the real and virtual worlds ( Mu et al, 2021 ; Ben Atitallah, Driss & Almomani, 2022 ). New business models and global interactions emerge as people, products, technologies, and the internet become more interconnected ( Kumar, Janet & Neelakantan, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence-driven malware detection framework for internet of things environment

Alsubai

Dutta²,

Alnajim

et al. 2023

PeerJ Computer Science

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“…KITTI and BDD100K are collected from autonomous driving. In the field of autonomous driving, there are numerous vehicle detection works [23][24][25], especially in night-time scenes [26,27]. UA-DETRAC is collected from traffic intersection scenes.…”

Section: Vehicle Detection Datasetsmentioning

confidence: 99%

Select Informative Samples for Night-Time Vehicle Detection Benchmark in Urban Scenes

Wang,

Tu,

Al-Hassani

et al. 2023

Remote Sensing

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Night-time vehicle detection plays a vital role due to the high incidence of abnormal events in our daily security field. However, existing studies mainly focus on vehicle detection in autonomous driving and traffic intersection scenes, but ignore urban scenes. There are vast differences between these scenes, such as viewpoint, position, illumination, etc. In this paper, the authors present a night-time vehicle detection dataset collected from urban scenes, named Vehicle Detection in Night-Time Urban Scene (VD-NUS). The VD-NUS dataset consists of more than 100 K challenging images, comprising a total of about 500 K labelled vehicles. This paper introduces a vehicle detection framework via an active auxiliary mechanism (AAM) to reduce the annotation workload. The proposed AAM framework can actively select the informative sample for annotation by estimating its uncertainty and locational instability. Furthermore, this paper proposes a computer-assisted detection module embedded in the AAM framework to help human annotators to rapidly and accurately label the selected data. AAM outperformed the baseline method (random sampling) by up to 0.91 AP and 3.0 MR−2 on the VD-NUS dataset.

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“…Currently, image enhancement algorithms remain a thriving area of research, employing various approaches like spatial domain, Retinex theory, frequency domain, etc. These algorithms mainly focus on denoising or enhancing brightness and contrast to make lowlight images appear brighter and more natural [2,3], such as histogram equalization and grayscale transformation, which can unify the histogram to achieve higher contrast. However, these methods tend to over-enhance certain areas, making them less ideal.…”

Section: Introductionmentioning

confidence: 99%

Non-Uniform-Illumination Image Enhancement Algorithm Based on Retinex Theory

Ji,

Guo,

Zhang

et al. 2023

Applied Sciences

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To address the issues of fuzzy scene details, reduced definition, and poor visibility in images captured under non-uniform lighting conditions, this paper presents an algorithm for effectively enhancing such images. Firstly, an adaptive color balance method is employed to address the color differences in low-light images, ensuring a more uniform color distribution and yielding a low-light image with improved color consistency. Subsequently, the image obtained is transformed from the RGB space to the HSV space, wherein the multi-scale Gaussian function is utilized in conjunction with the Retinex theory to accurately extract the lighting components and reflection components. To further enhance the image quality, the lighting components are categorized into high-light areas and low-light areas based on their pixel mean values. The low-light areas undergo improvement through an enhanced adaptive gamma correction algorithm, while the high-light areas are enhanced using the Weber–Fechner law for optimal results. Then, each block area of the image is weighted and fused, leading to its conversion back to the RGB space. And a multi-scale detail enhancement algorithm is utilized to further enhance image details. Through comprehensive experiments comparing various methods based on subjective visual perception and objective quality metrics, the algorithm proposed in this paper convincingly demonstrates its ability to effectively enhance the brightness of non-uniformly illuminated areas. Moreover, the algorithm successfully retains details in high-light regions while minimizing the impact of non-uniform illumination on the overall image quality.

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Low and non-uniform illumination color image enhancement using weighted guided image filtering

Cited by 14 publications

References 51 publications

Artificial intelligence-driven malware detection framework for internet of things environment

Artificial intelligence-driven malware detection framework for internet of things environment

Select Informative Samples for Night-Time Vehicle Detection Benchmark in Urban Scenes

Non-Uniform-Illumination Image Enhancement Algorithm Based on Retinex Theory

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