ACM SIGGRAPH 2008 papers
A content-adaptive method for single image dehazing
PCM'10 Proceedings of the Advances in multimedia information processing, and 11th Pacific Rim conference on Multimedia: Part II
A fast semi-inverse approach to detect and remove the haze from a single image
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Viewing scenes occluded by smoke
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
Longitudinal and lateral control in automated highway systems: their past, present and future
ICIRA'11 Proceedings of the 4th international conference on Intelligent Robotics and Applications - Volume Part II
Visibility cameras: where and how to look
Proceedings of the 1st ACM international workshop on Multimedia analysis for ecological data
Image dehazing algorithm based on atmosphere scatters approximation model
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
Adaptive background defogging with foreground decremental preconditioned conjugate gradient
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
Weighted haze removal method with halo prevention
Journal of Visual Communication and Image Representation
Image dehazing based on haziness analysis
International Journal of Automation and Computing
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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.