Variational Single Image Dehazing for Enhanced Visualization

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Variational Single Image Dehazing for Enhanced Visualization

Faming Fang; Tingting Wang; Yang Wang; Tieyong Zeng; Guixu Zhang

In this paper, we investigate the challenging task of removing haze from a single natural image. The analysis on the haze formation model shows that the atmospheric veil has much less relevance to chrominance than luminance, which motivates us to neglect the haze in the chrominance channel and concentrate on the luminance channel in the dehazing process. Besides, the experimental study illustrates that the YUV color space is most suitable for image dehazing. Accordingly, a variational model is proposed in the Y channel of the YUV color space by combining the reformulation of the haze model and the two effective priors. As we mainly focus on the Y channel, most of the chrominance information of the image is preserved after dehazing. The numerical procedure based on the alternating direction method of multipliers (ADMM) scheme is presented to obtain the optimal solution. Extensive experimental results on real-world hazy images and synthetic dataset demonstrate clearly that our method can unveil the details and recover vivid color information, which is competitive among many existing dehazing algorithms. Further experiments show that our model also can be applied for image enhancement. 

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