Efficient Traffic Video Dehazing Using Adaptive Dark Channel Prior and Spatial–Temporal Correlations

The advanced driving assistant system (ADAS) is an important vehicle safety technology that can effectively reduce traffic accidents. This system can perceive information about the surrounding environment through in-vehicle cameras. However, these cameras are easily affected by severe weather conditions, such as those involving fog, rain, and snow. The quality of the images acquired by the system is degraded, and the function of the ADAS is thus weakened. In response to this problem, we propose a comprehensive imaging model that can represent the features of fog, rain streaks, raindrops and snowflakes in an image. Subsequently, an algorithm called RASWNet is proposed, which can remove all severe weather features from a degraded image. Based on the generative adversarial network, RASWNet combines the focus capture ability of a visual attention mechanism, the memory ability of the recurrent neural network and the feature extraction ability of the dense blocks approach. We verify the network structure through several ablation studies and use various synthetic and real images to test it. The results of these experiments show that our algorithm is not only better than the commonly used algorithms in terms of its clarity enhancement capacity but is also suitable for all severe weather conditions. INDEX TERMS Generative adversarial network, remove all severe weather features, degraded image, RASWNet, visual attention mechanism.

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“…The first imaging model is for foggy images, and the commonly used atmospheric scattering model [30], [42], [28] is as follows:…”

Section: Severe Weather Imaging Modelsmentioning

confidence: 99%

RASWNet: An Algorithm That Can Remove All Severe Weather Features from a Degraded Image

Gao

Long

et al. 2020

IEEE Access

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“…Dehazing methods can be divided [ 2 ] into three categories: image enhancement [ 8 , 9 , 10 ], image fusion [ 11 ] and image restoration-based methods [ 12 ]. Another classification is made according to how many images are used: single image methods or multiple image methods [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Single Image Dehazing Algorithm Analysis with Hyperspectral Images in the Visible Range

Martínez-Domingo

Valero

Nieves

et al. 2020

Sensors

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In foggy or hazy conditions, images are degraded due to the scattering and attenuation of atmospheric particles, reducing the contrast and visibility and changing the color. This degradation depends on the distance, the density of the atmospheric particles and the wavelength. We have tested and applied five single image dehazing algorithms, originally developed to work on RGB images and not requiring user interaction and/or prior knowledge about the images, on a spectral hazy image database in the visible range. We have made the evaluation using two strategies: the first is based on the analysis of eleven state-of-the-art metrics and the second is two psychophysical experiments with 126 subjects. Our results suggest that the higher the wavelength within the visible range is, the higher the quality of the dehazed images. The quality increases for low haze/fog levels. The choice of the best performing algorithm depends on the criterion prioritized by the metric design strategy. The psychophysical experiment results show that the level of agreement between observers and metrics depends on the criterion set for the observers’ task.

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“…On a related issue of traffic videos, [ 8 ] sought to tackle the issue of restoring traffic videos with different degrees of haziness in a real-time and adaptive manner. They work proposed an efficient traffic video dehazing method using adaptive dark channel prior and spatial–temporal correlations.…”

Section: Safety and Securitymentioning

confidence: 99%

Intelligent Transportation Related Complex Systems and Sensors

Kyamakya

Chedjou

Machot

et al. 2021

Sensors

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Efficient Traffic Video Dehazing Using Adaptive Dark Channel Prior and Spatial–Temporal Correlations

Cited by 26 publications

References 32 publications

RASWNet: An Algorithm That Can Remove All Severe Weather Features from a Degraded Image

RASWNet: An Algorithm That Can Remove All Severe Weather Features from a Degraded Image

Single Image Dehazing Algorithm Analysis with Hyperspectral Images in the Visible Range

Intelligent Transportation Related Complex Systems and Sensors

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