基于自适应伽玛校正的去雾算法

Defog Algorith Based on Adaptive Gamma Correction

考虑到目前去雾算法复杂度高、效果不稳定和处理结果模糊等问题,为了更好地提高雾霾环境的图像能见度,提出基于自适应伽马校正的去雾算法。首先利用暗原色优先模型构造透射率图,然后通过对比度评估算法来计算环境光系数,以获取初始去雾图像,再利用自适应伽玛校正算法增强初始去雾图像的亮度和对比度,从而最大限度地提高事物的能见度。结合实例,对算法进行了详细的阐述,实验结果表明,该算法切实可行。

Fog and haze environment will greatly reduce the visibility of the image, the fog algorith has become a hot spot in thefield of image sensor. Aiming at the problems of the existing defog algorith including high complexities, instable processing effects,and dim result images, to better improve the visibility of fog and haze environment a defog algorith based on adaptive gamma correction is proposed. Firstly the dark channel prior model is utilized to produce the transmittance map. And then calculate the coefficient of ambient light through the contrast evaluation algorithm, in order to obtain the initial image to the fog. The next step is usingthe algorith based on adaptive gamma correction to enhance the brightness and contrast of the initial defogging image, so as to maximize the visibility. The algorithm is described in detail, and the experimental results show that the algorithm is feasible.