Abstract
For ordinary degraded images, there have been many effective methods to correct them, one of which is the histogram equalization. But if histogram equalization or local histogram equalization approach is applied directly on foggy images in traffic, a special class of degraded images, it will lead some distortion. The degraded model based on scene depth estimation is an effective algorithm for this situation. However, the scene depth estimation is a difficult task and its computational complexity is high. In this paper, we present a new algorithm for foggy image restoration in traffic, which is based on histogram equalization. The useful information in low brightness area is preserved and the histogram of the sky area is translated and narrowed down. The enhancement area of useful information is enlarged and its computational complexity is low compared with the degraded model.
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