Although image dehazing algorithms based on a single Generative Adversarial Network (GAN) effectively remove haze, they still encounter issues such as dark brightness and blurred details. This paper proposes a dual-generator dehazing structure based on a GAN network, integrating channel and spatial attention mechanisms. By generating dehazed and brightness-restored images in both RGB and YUV color spaces and adjusting the contrast between the two sets of images, the brightness and details are effectively restored, resulting in final output images that are more natural and consistent. Experimental results demonstrate that this method offers significant improvements over existing dehazing algorithms on multiple public datasets, exhibiting superior performance in both subjective visual effects and objective metrics.