To address the loss of details, low brightness, and contrast in existing fusion algorithms, the article proposes the Fusion of Low-Light visible light and infrared images based on Parallel Networks (PNLLFusion). PNLLFusion aims to maximize the preservation of detail information from the source images and enhance brightness and contrast. This method implements parallel fusion and brightness enhancement, reducing information loss caused by incompatibility between enhancement and fusion algorithms. Additionally, residual structures are added on the Squeeze-and-Excitation (SE) module, and gradient computation is incorporated into the self-attention network to preserve more texture and edge information. The effectiveness of the method is validated on the LLVIP dataset and TNO dataset. Experimental results demonstrate that compared to classical fusion algorithms, this method can preserve more detail information from the source images in low-light environments, while also improving image contrast and brightness. It achieves good or comparable results in both subjective and objective evaluations.