In recent years, deep-learning-based pixel-level unified image fusion methods have received more and more attention due to their practicality and robustness. However, they usually require a complex network to achieve more effective fusion, leading to high computational cost. To achieve more efficient and accurate image fusion, a lightweight pixel-level unified image fusion (L-PUIF) network is proposed. Specifically, the information refinement and measurement process are used to extract the gradient and intensity information and enhance the feature extraction capability of the network. In addition, these information are converted into weights to guide the loss function adaptively. Thus, more effective image fusion can be achieved while ensuring the lightweight of the network. Extensive experiments have been conducted on four public image fusion datasets across multimodal fusion, multifocus fusion, and multiexposure fusion. Experimental results show that L-PUIF can achieve better fusion efficiency and has a greater visual effect compared with state-of-the-art methods. In addition, the practicability of L-PUIF in high-level computer vision tasks, i.e., object detection and image segmentation, has been verified.