Contrast restoration of road images taken in foggy weather

Halmaoui, Houssam; Cord, A.; Hautiére, Nicolas

doi:10.1109/iccvw.2011.6130501

Cited by 14 publications

(6 citation statements)

References 16 publications

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“…The techniques to eliminate this degradation, known as dehazing (or defogging), can improve the visual quality of images for computer-aided applications and human interpretation and are, consequently, crucial in many applications such as air and maritime transport, surveillance, driver assistance systems, remote sensing, agronomy, archaeology, astronomy, etc. [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ].…”

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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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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“…1), produced by a dedicated software which could also simulate various fog densities [25]. These images were displayed either directly or after a dehazing image processing step [9]. The next two experiments used simpler conditions, with uniform discs as targets, and a uniform background.…”

Section: Methodsmentioning

confidence: 99%

“…While it is under low visibility that the driver most needs help, it is also then that the ADAS performance deteriorates. Fortunately, under foggy conditions, it is possible to detect and characterise fog by a vision algorithm, then to restore the signal at the output of the camera sensor, providing a better signal quality for the ADAS [9]. Together with improving ADAS performance in fog, the restored images with improved visibility may also be displayed to the driver with a head up display (HUD) to improve his perception of the road scene [10].…”

Section: Introductionmentioning

confidence: 99%

“…Assuming that the camera has a linear response, one can invert (1), so that the restoration formula is

L_{0} = L e^{k d} + L_{s} false(1 - e^{k d} false)

Equation (3) applies to the pixel's intensity, assuming that this intensity is proportional to L . In the following, image dehazing is computed with a previously published method [9], but other algorithms may apply as well [10].…”

Section: Introductionmentioning

confidence: 99%

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Quantitative model of the driver's reaction time during daytime fog – application to a head up display‐based advanced driver assistance system

Halmaoui

Joulan

Hautiére

et al. 2015

IET Intelligent Transport Systems

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Road accidents because of fog are relatively rare but their severity is greater and the risk of pile-up is higher. However, processing the images grabbed by cameras embedded in the vehicles can restore some visibility. Tarel et al. (2012) proposed to implement head up displays (HUD) to help drivers anticipate potential collisions by displaying dehazed images of the road scene. In the present study, three experiments have been designed to quantify the expected gain of such a system in terms of the driver's reaction time (RT). The first experiment compares the RT with and without dehazing, giving quantitative evidence that such an advanced driving assistance system (ADAS) may improve road safety. Then, based on a modified Piéron's law, a quantitative model is proposed, linking the RT to the target visibility (V t ), which can be computed from onboard camera images. Two additional experiments have been conducted, giving evidence that the proposed RT model, computed from V t , is robust with respect to contextual cues, to contrast polarity and to population sample. The authors finally propose to use this predictive model to switch on/off the proposed HUD-based ADAS.

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“…In computer vision, graphic systems and other related applications, image corruption persistently continues to be a significant challenge to be resolved. Outdoor systems such as traffic monitoring [5], autonomous self-driving cars [6], outdoor security and safety surveillance [7] are commonly affected by image corruption due to adverse weather conditions. Under such uncontrolled weather or environment of outdoor systems, the image acquisition process is certainly influenced adverse weather and the aerosol such as haze, fog, smoke and other atmospheric particles.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Night-Time Single Image Dehazing

Owusu-Agyeman¹,

Xie²,

Okae³

2019

JCC

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Images acquired under deprived weather environment are frequently corrupted due to the presence of haze, mist, fog or other aerosols in a form of noise. Haze elimination is essential in computer vision and computational photography applications. Generally, there is the existence of numerous approaches towards haze removal which are mostly meant for hazy images under daytime environments. Although the potency of these proposed approaches has been comprehensively established on daylight hazy images. However these procedures inherit significant limitations on images influenced by night-time hazy environments. Since night time haze removal dehazing remains an ill-posed problem, we proposed a novel method for night-time single image dehazing which is efficient under night-time environments. The proposed scheme is a dark channel-based local image dehazing procedure that locally estimates the atmospheric intensity for each selected mask on a corrupted image independently and not the entire image. This is done in order to overcome the challenge of night-scenes that are exposed to multiple/artificial lights source and spatially non-uniform environmental illumination. We performed an adaptive filtering on the combined dehazed masks to improve the degraded image. We validated the supremacy of the proposed approach in terms of speed and robustness through computer-based experiments. Conclusively, we displayed comparison results with state-of-the-art and extensively emphasized the comparative advantage of our scheme.

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Contrast restoration of road images taken in foggy weather

Abstract: Abstract

Cited by 14 publications

References 16 publications

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

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

Quantitative model of the driver's reaction time during daytime fog – application to a head up display‐based advanced driver assistance system

A Novel Method for Night-Time Single Image Dehazing

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