基于多先验约束的雾霾图像复原

Haze Image Restoration Based on Multi-Prior Constraints

针对目前单幅雾霾图像复原算法使用单一先验而产生先验盲区的问题,提出一种使用多先验约束的雾霾图像复原算法。首先,提出饱和度先验,使用定义的调节系数简化粗略传递图的求解过程;其次,在马尔科夫随机场模型中,使用颜色衰减先验进行约束并优化调节系数,求解得到精确传递图;接着,利用明暗像素先验得到精准的大气光;最后复原无雾图像。实验结果表明,其他算法与所提算法相比,有效细节强度分别降低了24.9%,51.4%,41.5%,39.3%,色调还原度分别降低了21.4%,24.8%,24.1%,29.5%,由此可知使用所提算法复原图像,图像中的有效细节信息丰富,色调自然,具有较强的适用性。

This study focuses on the problem of a priori blind zone,which is generated by the current single-frame haze image restoration algorithm using a single prior.To address this problem,a haze image restoration algorithm using multiple prior constraints is proposed.First,the saturation prior is proposed,and the defined adjustment coefficient is used to simplify the process of solving the rough transfer diagram.Second,in the Markov random field model,the color attenuation prior is used to constrain and optimize the adjustment coefficient to obtain an accurate transfer diagram.Then,the light and dark pixels are used to obtain accurate atmospheric light a priori.Finally,the fog-free image is restored.Experimental results reveal that compared with other algorithms,Compared with the proposed algorithm,other algorithms have reduced the effective detail intensity by 24.9%,51.4%,41.5%,and 39.3%,respectively,and the hue reproduction has decreased by 21.4%,24.8%,24.1%,and 29.5%,respectively.The proposed algorithm successfully restores the image.Consequently,the effective detail information in the image becomes rich,and the color tone becomes natural.Moreover,it enables the image to have strong applicability.