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The fusion of infrared and visible images produces a complementary image that captures both infrared radiation information and visible texture structure details using the respective sensors. However, the current deep-learning-based fusion approaches mainly tend to prioritize visual quality and statistical metrics, leading to an increased model complexity and weight parameter sizes. To address these challenges, we propose a novel dual-light fusion approach using adaptive DenseNet with knowledge distillation to learn and compress from pre-existing fusion models, which achieves the goals of model compression through the use of hyperparameters such as the width and depth of the model network. The effectiveness of our proposed approach is evaluated on a new dataset comprising three public datasets (MSRS, M3FD, and LLVIP), and both qualitative and quantitative experimental results show that the distillated adaptive DenseNet model effectively matches the original fusion models’ performance with smaller model weight parameters and shorter inference times.