The current infrared and visible light image fusion algorithms often fail to fully extract image features, resulting in the loss of detail information. In real-world scenarios, infrared and visible light images are typically unregistered, and existing registration algorithms still suffer from artifacts and biases. To address these issues, this paper proposes an adaptive feature enhancement multi-scale infrared and visible light image registration and fusion algorithm. First, multi-scale convolutional kernels and dense connections are used in the registration network to extract features at different scales and prevent information loss. Additionally, an ORB feature point detection algorithm and a designed feature enhancement module are introduced to fully extract features and adapt to complex environments. Secondly, a lighting enhancement module is designed by incorporating channel attention and self-learning parameters to improve the information expression of visible light images. Then, in the fusion network, adaptive multi-scale pooling convolutions are designed using different pooling strategies and variable convolutions to extract detail information at multiple scales. An EMA feature fusion module is designed to integrate local and global features. Finally, a flow consistency loss function is introduced to minimize registration errors. To better validate the practical applicability of the proposed method, an infrared and visible light image dataset is established. Comparative and ablation experiments are conducted on the public datasets TNO, Roadscene, and a self-constructed dataset. The experimental results show that, in terms of subjective evaluation, the registered images have minimal bias and no artifacts, while the fused images are clear and visible. On objective evaluation, it improves about 20%, 7%, 4%, 15%, and 8% on the metrics MSE, MI, NCC, SD, and EN compared to other algorithms. Additionally, target detection performance experiments on YOLOv8 show that the fusion results exhibit good detection performance.