基于雾气特征与不等关系约束的去雾方法

Haze removal based on haze feature and inequality relation constraint

摘要:

针对现有去雾算法未充分考虑图像雾气信息、复原图像细节模糊等问题,提出一种新颖的反映图像雾信息分布的雾气特征图,并采用不等关系约束方法提高图像质量。首先,提取退化图像的极值通道以实现雾气信息的粗略估计,并通过L-1正则化对其进行优化从而得到雾气特征图。其次,提出一种基于雾气特征的初级大气光幕函数,通过对颜色通道和大气光幕作深入分析,利用均值不等式获得约束后的退化场景大气光幕。最后,利用雾气特征图对局部大气光进行改进,并基于大气散射模型实现图像去雾。将所提算法在真实雾图和合成数据集雾图上与其他经典方法进行比较分析,可以发现,所提算法在单幅图像去雾中展现了较好的性能,且在夜间雾图复原中更具优势。

Abstract:

Aiming at the problems that the existing dehazing algorithms do not fully consider the fog information of haze image and the blurred details of the restored image,a novel haze feature map reflecting the distribution of fog information is proposed, and an inequality relation constraint method is adopted to enhance image quality.First,the extreme value channels of degraded image are extracted to achieve a rough estimation of fog information, and optimized by using L-1 regularization to obtain a haze feature map.After that,a primary atmospheric light veil function based on haze feature map is presented.Through in-depth analysis of color channel and atmospheric light veil, the constrained atmospheric light veil is obtained by using the mean value inequality.Finally,the local atmospheric light is improved by using the haze feature map,and haze removal is achieved based on the atmospheric scattering model.Compared with other existing state-of-the-art methods on both real-world and synthetic datasets haze images,our method shows favorable performance for single image dehazing especially in night image dehazing.