Joint image dehazing and contrast enhancement using the HSV color space

Many real images are shot under hazy conditions and need to be processed for better quality. So far the majority of the dehazing methods try to faithfully invert the standard hazy image degradation model. In such an approach, not only is the basic problem theoretically unsolvable, but the parameters used in many methods are often hard to estimate accurately. In this paper we make two contributions. We first derive the standard haze model in the HSV color space, which is preferred over the traditional RGB color space for contrast enhancement due to its robustness to color distortion. Specifically, we show that under generally valid assumptions, the H channel is invariant to haze degradation. The S channel can be recovered when the ambient light and the medium transmission coefficients are estimated as in the traditional approach. Only the V channel takes the same form of the haze model as for the RGB channels. Secondly, we show that the standard image dehazing problem can be viewed as a type of image contrast enhancement. It is then possible to place a less stringent requirement on faithful parameter estimation in the framework of contrast enhancement. Based on such insight, we propose an algorithm that combines the image dehazing and contrast enhancement seamlessly. The major advantage of this new approach is that instead of degrading the processed image quality, the inaccurately estimated parameters with intentional bias tend to enhance the image contrast. Experimental results show that the new approach enjoys a large performance advantage over existing methods.