Abstract: The existence of fog has seriously affected the human outdoor activities. For dehazing images, which is a common computer vision task, the academic circle has carried out extensive research. However, it still faces severe challenges such as the limited ability of real fog image to dehaze. Therefore, this study proposes an end-to-end adaptive feature attention network based on multi-step fusion. The adaptive feature attention module can adaptively expand the receptive field, obtain the key structure information in the space, and extract more complex features. In addition, considering the lack of connection between low-level and high-level features acquired in the network, the multi-step fusion module is also completed, which can make the features of different levels in the network effectively complement each other in the process of image restoration. Besides, by reducing network parameters, the computing resources required by the optimized network structure are greatly reduced. For dense haze and NH haze data sets with real haze, the research method obtains high peak signal-to-noise ratio（PSNR） of 16.23 dB and 21.38 dB and structural similarity（SSIM） of 0.521 3 and 0.714 4. The actual visual effects are better than other selected advanced technologies.