ArticleSensors (Basel, Switzerland)2022
Image Dehazing Using LiDAR Generated Grayscale Depth Prior.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Restoration of Binocular Images Degraded by Optical Scattering through Estimation of Atmospheric Coefficients.Sensors (Basel, Switzerland) · 2023Article
- Investigating the Improvement of Autonomous Vehicle Performance through the Integration of Multi-Sensor Dynamic Mapping Techniques.Sensors (Basel, Switzerland) · 2023Article
- Using Whale Optimization Algorithm and Haze Level Information in a Model-Based Image Dehazing Algorithm.Sensors (Basel, Switzerland) · 2023Article
- Single Image Dehazing Using Global Illumination Compensation.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
3 authors.
Funding
Abstract
In this paper, the dehazing algorithm is proposed using a one-channel grayscale depth image generated from a LiDAR point cloud 2D projection image. In depth image-based dehazing, the estimation of the scattering coefficient is the most important. Since scattering coefficients are used to estimate the transmission image for dehazing, the optimal coefficients for effective dehazing must be obtained depending on the level of haze generation. Thus, we estimated the optimal scattering coefficient for 100 synthetic haze images and represented the distribution between the optimal scattering coefficient and dark channels. Moreover, through linear regression of the aforementioned distribution, the equation between scattering coefficients and dark channels was estimated, enabling the estimation of appropriate scattering coefficient. Transmission image for dehazing is defined with a scattering coefficient and a grayscale depth image, obtained from LiDAR 2D projection. Finally, dehazing is performed based on the atmospheric scattering model through the defined atmospheric light and transmission image. The proposed method was quantitatively and qualitatively analyzed through simulation and image quality parameters. Qualitative analysis was conducted through YOLO v3 and quantitative analysis was conducted through MSE, PSNR, SSIM, etc. In quantitative analysis, SSIM showed an average performance improvement of 24%.
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