针对暗通道先验算法处理大片天空区域存在复原图像的可视化效果较差和图像细节信息不丰富等问题,提出一种基于多尺度Retinex和暗通道的自适应图像去雾算法。采用Canny算子对亮度分量进行边缘检测并利用多尺度Retinex算法消除亮度分量,采用交叉双边滤波优化暗通道的先验理论获得粗略估计的透射率,采用四叉树子空间搜索法选取全局大气光值。为了消除图像中复原图像整体较暗以及无法显示细节信息的现象,使用二维伽马函数校正亮度值,最终得到复原后的去雾图像。实验结果表明,所提算法可以有效恢复有雾图像的细节信息,去雾较为彻底,整体平滑,色彩明亮度较好,图像清晰自然。

To address the problems of poor visualization of restored images and the insufficient image details obtained using dark channel prior algorithms in the processing of large areas of sky, we propose an adaptive image defogging algorithm based on multi-scale Retinex and dark channels. A Canny operator is used to detect the edge of the brightness component and a multi-scale Retinex algorithm is used to eliminate the brightness component. To optimize the prior theory of the dark channel and obtain a rough estimate of the transmittance, we use a cross-bilateral filter. We use the quadtree subspace search method to select the global atmospheric light value. To eliminate the overall darkness of the restored image and enable the display of detailed image information, a 2D gamma function is used to correct the brightness value and restore the defogged image. The experimental results show that the proposed algorithm can effectively restore detailed information in a foggy image to obtain images that feature thorough dehazing, overall smoothness, better color brightness, and a clear and natural appearance.