Aiming at the problem of blurred areas at the imaging edge caused by the fusion of infrared and visible light images, a fusion algorithm of infrared image (IFR) and visible light image (VBI) driven by the average gradient energy in NSCT domain is proposed. Firstly, NSCT transformation is performed on IFR and VBI respectively. The low frequency, intermediate frequency, and high frequency subband coefficients are obtained. The low frequency fusion adopts the method of taking the maximum average gradient energy (Average Gradient Energy, AVGE) for fusion, the intermediate frequency fusion adopts the maximum value of the spatial frequency (Spatial Frequency, STF) for fusion, and the high frequency A fusion method of Average Gradient Energy Pulse Coupled Neural Network (AVGE-PCNN) is proposed for fusion, and the final fusion is obtained by using Inverse Nonsubsampled Contourlet Transform (INSCT) In the experiment, three groups of IFR and VSI images of different scenes were used for fusion processing. The comparative experiments proved that the fusion method proposed in this paper has a good effect in improving image edge blur, and the subjective evaluation and objective evaluation are better than DWT, DTCWT, and NSCT algorithms.