结合Lab空间和单尺度Retinex的自适应图像去雾算法

针对暗通道先验算法在大片天空区域透射率估计过小及景深突变处出现Halo效应的问题，提出一种结合Lab空间和单尺度Retinex的图像去雾算法。将RGB图像转换至Lab空间提取出亮度分量，利用Canny算子对亮度分量提取边缘信息，丰富恢复图像细节；利用单尺度Retinex对非边缘区域进行高斯自适应滤波估计出优化后的亮度分量，获得“伪”去雾图像，得到粗略的透射率；利用交叉双边滤波优化透射率消除Halo效应；最后根据大气散射模型恢复出无雾图像。实验结果表明，该方法恢复出的图像细节明显，整体平滑，且对含大片天空区域的图像也有较好的恢复效果。

Adaptive image defogging algorithm combined with lab space and single-scale Retinex

Aiming at the small transmittance estimate and the Halo effect caused by the sudden change in the depth of field of the dark channel prior algorithm in the large sky area,an image defogging algorithm combined with lab space and single-scale retinex was proposed.Firstly,the RGB image was transferred to the lab space to extract the luminance component,and the edge information of this was extracted by the Canny operator to enrich the details of the restored image.Secondly,the single-scale retinex was used to perform a Gaussian adaptive filtering of the non-edge region to estimate the optimized luminance component,and the“false”defogging image as well as the rough transmittance was obtained.Then,the cross-bilateral filtering was used to eliminate the Halo effect by optimizing the transmittance.Finally,the defogging image was recovered on the basis of the atmospheric scattering model.The experimental results show that the image recovered by this method is clear and smooth overall,and also has a good recovery effect on images with large sky areas.