Abstract

Outdoor imaging is plagued by poor visibility conditions due to atmospheric scattering, particularly in haze. A major problem is spatially-varying reduction of contrast by stray radiance (airlight), which is scattered by the haze particles towards the camera. Recent computer vision methods have shown that images can be compensated for haze, and even yield a depth map of the scene. A key step in such a scene recovery is subtraction of the airlight. In particular, this can be achieved by analyzing polarization-filtered images. However, the recovery requires parameters of the airlight. These parameters were estimated in past studies by measuring pixels in sky areas. This paper derives an approach for blindly recovering the parameter needed for separating the airlight from the measurements, thus recovering contrast, with neither user interaction nor existence of the sky in the frame. This eases the interaction and conditions needed for image dehazing, which also requires compensation for attenuation. The approach has proved successful in experiments, some of which are shown here.

Keywords

HazeRadianceComputer visionArtificial intelligenceComputer sciencePixelVisibilityContrast (vision)SkyDiffuse sky radiationAttenuationImage restorationRemote sensingScatteringImage processingOpticsImage (mathematics)GeologyPhysics

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Publication Info

Year
2006
Type
article
Volume
2
Pages
1984-1991
Citations
510
Access
Closed

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Cite This

S.C. Shwartz, Einav Namer, Yoav Y. Schechner (2006). Blind Haze Separation. 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) , 2 , 1984-1991. https://doi.org/10.1109/cvpr.2006.71

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DOI
10.1109/cvpr.2006.71

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