In recent years, the field of computer vision has developed rapidly, so it is particularly important to obtain high-quality image information. Image defogging is a technique widely used to enhance the visual quality of images insevere weather conditions. The dark channel prior method achieves image defogging by estimating atmospheric light. Although it has achieved good results, there are still problems that the atmospheric light is overestimated and is not suitable for large white areas. Aiming at the existing image defogging problems, we propose a deep learning method based on the improved DehazeNet for image defogging in this study. This method introduces a depthwise separable convolutional layer inestimating the transmission map. In order to enlarge the receptive field, dilated convolutionis used in atmospheric light. The experimental results show that the improved defogging algorithm in this study can effectively restore the foggy images and improve the image quality and has an excellent defogging effect in both quantitative and qualitative evaluation compared with other comparison algorithms.