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.