In nighttime-driving scenes, the image quality deteriorates significantly owing to insufficient light and haze, which poses severe challenges to the driver and automatic drive system. Hence, a novel image-dehazing algorithm for nighttime-driving scenes is proposed. Instead of relying on the classical a priori theory, the algorithm considers the nighttime haze image as a superposition of haze and background layers from the reconstruction perspective, and a lightweight super-resolution reconstruction dehazing network is proposed without using a physical imaging model. By introducing a haze feature-extraction network based on dilated convolution and an attention-mechanism module that uses the haze feature graph as supervisory information, the dehazing network efficiently removes the haze layer while preserving the image details and generating clear and high-contrast images. Comparison experiments with five state-of-the-art dehazing methods are conducted on two nighttime fog map datasets. The experiments show that the super-resolution reconstruction dehazing network performs better than all other nighttime dehazing models. The results of ablation experiments show that the attention module based on the supervision of haze features significantly improves the dehazing capability of the network. This study provides new ideas and methods for solving the image-quality problem in nighttime-driving scenarios, thus facilitating improvements to the driving safety and reliability of automatic driving systems.