<p>With the increasing frequency of natural disasters and health emergencies, wearable infrared thermal imaging devices are becoming more prevalent in fire protection and medical fields. However, these devices often face imaging performance challenges such as insufficient contrast, dark areas and blurred edges, which significantly limit their practical effectiveness. To tackle these challenges, we propose a novel unsupervised lightweight 3D convolutional network (UL3DCN) specifically designed for enhancing infrared images on wearable devices. In this framework, the task of infrared image enhancement is conceptualized as generating high dynamic range infrared images from the corresponding temperature sequences during thermal equilibrium. To achieve this, we first design a learnable dynamic filtering module tailored for simulating a series of infrared image sequences under varying temperature differences. This module extends a single image from the spatial domain into the spatio-temporal domain. Subsequently, we employ a lightweight 3D convolution module to effectively extract spatio-temporal information from the image sequence. Finally, inspired by Zero-DCE, we utilize the extracted information to estimate pixel values and high-order curves, thereby enhancing the infrared images. Comprehensive experimental results demonstrate that our method achieves outstanding performance and real-time capabilities. Additionally, the proposed UL3DCN model has been successfully integrated into a wearable infrared firefighting mask.</p>