Since existing low-illumination image enhancement networks have insufficient ability to perceive and express feature information of different scales, a low-illumination image enhancement network model based on pyramid asymptotic fusion was proposed. The network performed multiple down-sampling operations on the image to form a feature pyramid. It fused the feature maps at different scales by adding skip connections to three different branches of the feature pyramid. Fine recovery module further extracted the refined information, and restored the feature map to a normal light image. Results indicate that, the network model not only effectively enhances the brightness of the overall low-illumination image, but also maintains the detailed information and clear edge contours of the objects in the image. Moreover, it can effectively suppress the dark noise, and make the overall enhanced image realistic and natural.