Abstract
Abstract. Fog disturbs the proper image processing in many outdoor observation tools. For instance, fog reduces the visibility of obstacles in vehicle driving applications. Usually, the estimation of the amount of fog in the scene image allows to greatly improve the image processing, and thus to better perform the observation task. One possibility is to restore the visibility of the contrasts in the image from the foggy scene image before applying the usual image processing. Several algorithms were proposed in the recent years for defogging. Before to apply the defogging, it is necessary to detect the presence of fog, not to emphasis the contrasts due to noise. Surprisingly, few a reduced number of image processing algorithms were proposed for fog detection and characterization. Most are dedicated to static cameras and can not be used when the camera is moving. Daytime fog is characterized by its extinction coefficient, which is equivalent to the visibility distance. A visibility-meter can be used for fog detection and characterization, but this kind of sensor performs an estimation in a relatively small volume of air, and is thus sensitive to heterogeneous fog, and air turbulence with moving cameras. In this paper, we propose an original algorithm, based on entropy minimization, to detect fog and estimate its extinction coefficient by the processing of stereo pairs. This algorithm is fast, provides accurate results using low cost stereo camera sensor and, the more important, can work when the cameras are moving. The proposed algorithm is evaluated on synthetic and camera images with ground truth. Results show that the proposed method is accurate, and, combined with a fast stereo reconstruction algorithm, should provide a solution, close to real time, for fog detection and visibility estimation for moving sensors.
Highlights
A function of β, by: Even if fog is rare, it impacts the quality of the results of many images processing algorithms when applied to outdoor scenes, see Fig. 1
One possibility is to detect fog, turn on the fog lights and depending of the importance of the fog which is measured by the extinction coefficient β, adapt the strength of the fog lights, as demonstrated in ICADAC project1
An alternative is to display to the driver an image of the scene ahead of the vehicle after visibility restoration by image processing, see for instance (Tarel et al, 2012)
Summary
A function of β, by: Even if fog is rare, it impacts the quality of the results of many images processing algorithms when applied to outdoor scenes, see Fig. 1. Only few methods were proposed for detecting fog with a camera and for estimating the extinction coefficient β. Most of these methods apply only when the camera is static. In (Kopf et al, 2008), the attenuation model due to fog is estimated assuming that a 3D model of the scene is known This method is interesting since it shows that Koschmieder’s law is not always valid on long range distances due to heterogeneous fog. The proposed algorithm, combined with a real time stereo reconstruction algorithm, should provide a fast algorithm for estimating the extinction coefficient (which includes the detection of foggy weather using a threshold on the visibility distance) when the camera is moving. In section 4. the proposed algorithm is evaluated on a synthetic database for a moving camera and evaluated with a ground truth database for static camera images
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