Abstract: A novel image fusion method based on supervised intelligent learning is proposed in order to overcome the difficulty in the use of priori knowledge in image fusion. In this study, the images database for supervised learning is first constructed，then the model parameters trained with the available training datasets are used for the Takagi Sugeno Kang (TSK) fuzzy system model. Different from the classical method that needs to manage the different parameters setting manually, the proposed method can effectively preclude the problem in the optimal parameters setting. Meanwhile, some advantages are displayed in the fusion image quality and adaptation. The experimental studies on different types of images, both single and multi, also show the effectiveness of the method.