A real-time fast defogging system to clear the vision of driver in foggy highway using minimum filter and gamma correction

Mandal, Gouranga; De, Parthasarathi; Bhattacharya, Diptendu

doi:10.1007/s12046-020-1282-y

Cited by 6 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The median filter and gamma correction 48 This method takes less time to calculate and improves visual quality, but it does not maintain the corners in the reconstructed images…”

Section: Natural Imagesmentioning

confidence: 99%

Review and evaluation of recent advancements in image dehazing techniques for vision improvement and visualization

Shit,

Ray

2023

J. Electron. Imag.

View full text Add to dashboard Cite

Vision gets obscured in adverse weather conditions, such as heavy downpours, dense fog, haze, snowfall, etc., which increase the number of road accidents yearly. Modern methodologies are being developed at various academics and laboratories to enhance visibility in such adverse weather with the help of technologies. We review different dehazing techniques in many applications, such as outdoor surveillance, underwater navigation, intelligent transportation systems, object detection, etc. Dehazing is achieved in four primary steps: the capture of hazy images, estimation of atmospheric light with transmission map, image enhancement, and restoration. These four dehazing procedures allow for a step-by-step method for resolving the complicated haze removal issue. Furthermore, it also explores the limitations of existing deep learning-based methods with the available datasets and the challenges of the algorithms for enhancing visibility in adverse weather. Reviewed techniques reveal gaps in the case of remote sensing, satellite, and telescopic imaging. In the experimental analysis of various image dehazing approaches, one can learn the effectiveness of each phase in the image dehazing process and create more effective dehazing techniques.

show abstract

“…The median filter and gamma correction 48 This method takes less time to calculate and improves visual quality, but it does not maintain the corners in the reconstructed images…”

Section: Natural Imagesmentioning

confidence: 99%

Review and evaluation of recent advancements in image dehazing techniques for vision improvement and visualization

Shit,

Ray

2023

J. Electron. Imag.

View full text Add to dashboard Cite

show abstract

“…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%

Research on Defogging Algorithm for Surveillance Video

wang,

tian

et al. 2024

Preprint

View full text Add to dashboard Cite

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.

show abstract