Images of outdoor scenes are usually degraded by atmospheric particles, such as haze, fog and smoke, which fade the color and reduce the contrast of objects in the scene. This reduces image quality for manual or automated analysis in a variety of outdoor video surveillance applications, for example threat or anomaly detection. Current dehazing techniques, based on atmospheric models and frame-by-frame approaches, perform reasonably well, but are slow and unsuitable for real-time processing. This paper addresses the need for an online robust and fast dehazing algorithm that can improve video quality for a variety of surveillance applications. We build upon and expand state of the art dehazing techniques to develop a robust real-time dehazing algorithm with the following key characteristics and advantages: (1) We leverage temporal correlations and exploit special haze models to achieve 4× speed-up over the baseline algorithm  with no loss in detection performance, (2) We develop a pixel-by-pixel approach that allows us to retain sharp detail near object boundaries, which is essential for both manual and automated object detection and recognition applications, (3) We introduce a method for estimating global atmospheric lighting which makes it very robust for a variety of outdoor applications, and (4) We introduce a simple and effective sky segmentation method for improving the global atmospheric light estimation which has the effect of mitigating color distortion. We evaluate our approach on video data from multiple test locations, demonstrate both qualitative and quantitative improvements in image quality, and object detection accuracy.
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- Fast Image Dehazing Methods for Real-Time Video Processing
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