Real-time fast fog removal approach for assisting drivers during dense fog on hilly roads

Mandal, Gouranga; De, Parthasarathi; Bhattacharya, Diptendu

doi:10.1007/s12652-020-02734-0

Cited by 7 publications

(3 citation statements)

References 35 publications

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“…A(x, y) = CBF ksize (W)(x, y) (7) There is a certain amount of texture information in the filtered image, and the distribution of fog in the image is obtained by eliminating the texture part. The formula is as follows.…”

Section: Algorithm Principlementioning

confidence: 99%

“…Surveillance videos, characterized by dynamic moving objects and background scenes, pose difficulties for traditional dehazing algorithms in handling these complex variations [6]. This often leads to problems such as residual artifacts, distortion, or blurring [7]. Thus, addressing these challenges and developing dehazing algorithms suitable for surveillance videos remains an urgent and ongoing task [8].…”

Section: Introductionmentioning

confidence: 99%

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Research on Defogging Algorithm for Surveillance Video

wang,

tian

et al. 2024

Preprint

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This study presents a dehazing algorithm specifically tailored for improving the quality of surveillance images impaired by foggy weather. The core research areas encompass atmospheric light estimation and the dehazing process. First, a perfect white balance method is employed for preprocessing surveillance video frames to remove the color influence caused by fog in the images. Second, based on an in-depth analysis of surveillance scene characteristics, a depth-based atmospheric light estimation method is proposed to accurately obtain crucial atmospheric light information within fog. Subsequently, a cross Bilateral filter is introduced, carefully balancing filtering efficacy and edge preservation to effectively eliminate noise in foggy images. Finally, by subtracting the estimated atmospheric light component from surveillance video frames, clear dehazed surveillance images are obtained. The performance evaluation of the proposed algorithm on six datasets yields the following metrics: the highest Peak Signal-to-Noise Ratio (PSNR) is 8.507, the Structural Similarity Index (SSIM) is 0.826, the Universal Image Quality Index (UQI) is 0.732, and the information entropy is 7.663. These quantified performance indicators clearly illustrate the remarkable performance of the proposed algorithm in dehazing tasks, providing solid support for its practical application in the field of surveillance image enhancement.

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Section: Algorithm Principlementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Defogging Algorithm for Surveillance Video

wang,

tian

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Nowadays, remote sensing is playing an increasing role with a growing array of more accurate sensors, and its measurements are providing more and more information about fog and providing additional data for input to forecasting tools (Andrews & Bright, 2018;Brunner et al, 2016;Cracknell & Varotsos, 2011;Gultepe et al, 2019;Li et al, 2012;Lee et al, 2011). Furthermore, some progresses of fog monitoring and forecasting have been made using microwave radiation data (Martinet et al, 2020;Wang et al, 2018) and image recognition technology (Chaabani et al, 2018;Gao et al, 2019;Kwon 2004;Mandal et al, 2021;You et al, 2018), which can effectively make up for the lack of meteorological traffic observation along the expressway and improve the technical ability of fog monitoring and early warning systems. It is important to analyze the various characteristics of agglomerate fog for low In this article, Anhui Province was selected as the analysis area because it has a dense highway network and is prone to fog accidents and blocking events (Jiang et al, 2020;Liu et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Analysis of Agglomerate Fog Meteorological Characteristics in Anhui Province Based on Traffic Accident Data

Gao

Tian

et al. 2022

Pure Appl. Geophys.

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Using the data of five agglomerate-fog-related traffic accidents during 2014–2017 and fog-related expressway accidents during 2013–2018 assessed by the Anhui provincial traffic authorities and corresponding meteorological observation data, this article analyzes the spatio-temporal characteristics, geographical environment and weather background of agglomerate-fog-related accidents in detail and identifies the characteristics of changes in terms of visibility, humidity, temperature and wind when agglomerate fog occurs. Furthermore, an agglomerate fog forecast index has been established in Anhui Province to provide a reference basis for the agglomerate fog forecast, early warning and prevention.

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