This paper proposes a deep network-based defogging method to enhance the outdoor laser robot's vision perception capability, addressing the low visibility issues caused by atmospheric suspended particles, such as color bias, distortion, noise, etc., in the images. Firstly, flat and connected areas are identified and labeled as the sky light domain in the gradient domain and the gray domain, and the atmospheric light value is obtained using the quadtree method. Then, based on the input RGB feature map and the texture features extracted by the auto-encoder network, nonlinear mapping and transmission map reconstruction are completed. Finally, the estimated atmospheric light value and transmission rate are combined to restore the clear image using the atmospheric scattering model. Objective and subjective comparative experiments were conducted on our method, Retinex method, dual-threshold segmentation method, auto-encoder method, and AFF-Net method. The results showed that our method achieves superior objective indicators, both with and without reference, effectively preserving the original color tone of the real scene and generating clear, detailed images.