目的 雾天条件下采集的图像存在低对比度和低场景可见度的问题,传统的去雾算法易产生halo效应和色彩失真问题。为此,结合大气散射光特性提出一种基于相对总变差的图像复原方法。方法 首先从大气散射光与纹理信息无关的角度出发,利用相对总变差分离图像主结构和图像纹理信息准确估计大气耗散函数,通过引入一个自适应保护因子来避免复原图像的色彩失真问题,最后由大气散射模型计算复原图像并进行图像的亮度调整,得到一幅清晰无雾的图像。结果 通过与经典的去雾算法比较,表明该方法可以有效避免halo效应和天空颜色失真等不足,并且在图像的深度突变处也能得到很好的去雾效果。结论 实验表明该算法的场景适应能力较强,时间复杂度与图像的大小成线性关系,相比于前人的算法在计算速度上有一定的提高。

Objective A foggy image has low contrast and low visibility. Traditional dehazing algorithms suffer from the halo phenomenon and color distortion in image dehazing. In consideration of the characteristics of the atmospheric veil, an image restoration algorithm based on relative total variation is developed in this study. Method Given that atmospheric scattering light does not affect texture information, the atmospheric veil is estimated accurately according to the main structure and texture information separated by relative total variation. An adaptive protection factor is then utilized to avoid distorting the restored image. Finally, the ideal result is obtained with a physical model, and brightness is adjusted by a curve. Result Compared with state-of-the-art dehazing methods, the proposed method can avoid halo artifacts or color distortion and can achieve a good dehazing result at distant scenes and in areas where depth changes abruptly. Conclusion Experimental results show that the proposed method has robust scene adaptability and a fast computing rate because of the linear relation between time complexity and image size.