针对图像去雾过程中存在的天空区域失真、结果偏色和去雾不彻底等问题，提出一种通道差先验下的自适应高斯函数去雾算法。从雾天图像降质的本质出发，提出一种反映有雾图像与无雾图像内在联系的统计先验——通道差先验，通过该先验建立有雾图像和无雾图像的方程组，利用有雾图像的饱和度与亮度之差近似估计景深，设计了自适应标准差高斯函数求解方程组，获得初始透射率，经归一化处理后解决高亮区域“加雾”现象，并使用联合双边滤波深度优化透射率。利用多尺度滤波和几何均值优化局部大气光，结合大气散射模型获得去雾图像。实验结果表明：所提算法避免了天空区域失真，细节信息丰富，去雾效果显著，同时又能保持良好的图像颜色。


Addressing issues such as sky region distortion, color bias in results, and incomplete defogging in the process of image dehazing, an adaptive Gaussian function dehazing algorithm based on channel difference prior is proposed. Starting from the essence of degradation in foggy images, a statistical prior reflecting the intrinsic relationship between foggy and haze-free images, namely the channel difference prior, is introduced. Using this prior, a set of equations for foggy and haze-free images is established. The depth of field is approximately estimated using the difference between saturation and brightness of the foggy image. An adaptive standard deviation Gaussian function is designed to solve the equations and obtain the initial transmission map. After normalization, the "fog addition" phenomenon in bright regions is addressed, and joint bilateral filtering is used to further optimize the transmission map. Multi-scale filtering and geometric mean optimization are applied to refine the local atmospheric light, and the dehazed image is obtained by combining the atmospheric scattering model. Experimental results show that the proposed algorithm avoids distortion in the sky region, preserves rich detail information, and achieves significant dehazing effects while maintaining good image color.


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