Automatic fog detection and estimation of visibility distance through use of an onboard camera
Machine Vision and Applications
A real-time object detecting and tracking system for outdoor night surveillance
Pattern Recognition
International Journal of Computer Vision
Deep photo: model-based photograph enhancement and viewing
ACM SIGGRAPH Asia 2008 papers
Novel depth cues from light scattering
Image and Vision Computing
ACM SIGGRAPH ASIA 2008 courses
Removing image artifacts due to dirty camera lenses and thin occluders
ACM SIGGRAPH Asia 2009 papers
ACM SIGGRAPH 2009 Courses
Color image dehazing using the near-infrared
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Mitigation of visibility loss for advanced camera-based driver assistance
IEEE Transactions on Intelligent Transportation Systems
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
Layer-based single image dehazing by per-pixel haze detection
ACM SIGGRAPH ASIA 2010 Sketches
Multiclass object classification for real-time video surveillance systems
Pattern Recognition Letters
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
Real-time detection of small surface objects using weather effects
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Estimating meteorological visibility using cameras: a probabilistic model-driven approach
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
Enhancement of fog degraded images on the basis of histrogram classification
ICCOMP'10 Proceedings of the 14th WSEAS international conference on Computers: part of the 14th WSEAS CSCC multiconference - Volume II
Single image restoration of outdoor scenes
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II
Adaptive and nonlinear techniques for visibility improvement of hazy images
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
Viewing scenes occluded by smoke
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
A variational approach for exact histogram specification
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
International Journal of Computer Vision
Finger-vein ROI localization and vein ridge enhancement
Pattern Recognition Letters
Visibility cameras: where and how to look
Proceedings of the 1st ACM international workshop on Multimedia analysis for ecological data
Optimized contrast enhancement for real-time image and video dehazing
Journal of Visual Communication and Image Representation
Exact Histogram Specification for Digital Images Using a Variational Approach
Journal of Mathematical Imaging and Vision
Dense scattering layer removal
SIGGRAPH Asia 2013 Technical Briefs
Weighted haze removal method with halo prevention
Journal of Visual Communication and Image Representation
Enhanced fog detection and free-space segmentation for car navigation
Machine Vision and Applications
Towards finger-vein image restoration and enhancement for finger-vein recognition
Information Sciences: an International Journal
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Images of outdoor scenes captured in bad weather suffer from poor contrast. Under bad weather conditions, the light reaching a camera is severely scattered by the atmosphere. The resulting decay in contrast varies across the scene and is exponential in the depths of scene points. Therefore, traditional space invariant image processing techniques are not sufficient to remove weather effects from images. We present a physics-based model that describes the appearances of scenes in uniform bad weather conditions. Changes in intensities of scene points under different weather conditions provide simple constraints to detect depth discontinuities in the scene and also to compute scene structure. Then, a fast algorithm to restore scene contrast is presented. In contrast to previous techniques, our weather removal algorithm does not require any a priori scene structure, distributions of scene reflectances, or detailed knowledge about the particular weather condition. All the methods described in this paper are effective under a wide range of weather conditions including haze, mist, fog, and conditions arising due to other aerosols. Further, our methods can be applied to gray scale, RGB color, multispectral and even IR images. We also extend our techniques to restore contrast of scenes with moving objects, captured using a video camera.