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Outdoor photography and some computer vision tasks often suffer from bad weather conditions, observed objects lose visibility and contrast due to the presence of atmospheric haze, fog, and smoke. Here we propose a new method for real-time image dehazing. Based on a linear haze imaging model, for a single input image, we can esti-mate the global atmospheric light and extract the scene objects transmission from both single pixel and patch, and then combined by pixel saturation. To prevent artifacts, we refine the transmission using a cross-bilateral filter, and finally the haze-free frame can be restored. The whole process is highly parallelized, and can be easily implemented on modern GPUs to achieve real-time performance. Comparing with existing methods, our approach provides similar or better results with much less processing time. The proposed method can be further used for many applications such as outdoor surveil-lance, remote sensing, and intelligent vehicles. In addition, rough depth information of the scene can be obtained as a by-product.
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