ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Dynamic range independent image quality assessment
ACM SIGGRAPH 2008 papers
ACM SIGGRAPH 2008 papers
Haze removal for high-resolution satellite data: a case study
International Journal of Remote Sensing
Deep photo: model-based photograph enhancement and viewing
ACM SIGGRAPH Asia 2008 papers
SkyFinder: attribute-based sky image search
ACM SIGGRAPH 2009 papers
Color image dehazing using the near-infrared
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Contrast restoration of weather degraded images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Enhancing underwater images by fusion
ACM SIGGRAPH 2011 Posters
Single image restoration of outdoor scenes
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II
Image dehazing algorithm based on atmosphere scatters approximation model
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
Optimized contrast enhancement for real-time image and video dehazing
Journal of Visual Communication and Image Representation
Adaptive background defogging with foreground decremental preconditioned conjugate gradient
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
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In this paper we introduce a novel approach to restore a single image degraded by atmospheric phenomena such as fog or haze. The presented algorithm allows for fast identification of hazy regions of an image, without making use of expensive optimization and refinement procedures. By applying a single per pixel operation on the original image, we produce a 'semi-inverse' of the image. Based on the hue disparity between the original image and its semi-inverse, we are then able to identify hazy regions on a per pixel basis. This enables for a simple estimation of the airlight constant and the transmission map. Our approach is based on an extensive study on a large data set of images, and validated based on a metric that measures the contrast but also the structural changes. The algorithm is straightforward and performs faster than existing strategies while yielding comparative and even better results. We also provide a comparative evaluation against other recent single image dehazing methods, demonstrating the efficiency and utility of our approach.