To address the issue of edge and detail information degradation in infrared and visible image fusion caused by limitations such as information loss and data redundancy， a novel approach is presented. Traditional multi-scale domain fusion methods often result in the loss of edge information in both infrared and visible images. This study proposes a hybrid multi-scale decomposition model （HMSD） integrated with an enhanced pulse-coupled neural network （PCNN） for infrared and visible image fusion. The HMSD model， developed by combining the characteristics of fast alternating guided filtering （FAGF） and Gaussian filtering （GF）， decomposes the source images into a base layer and three feature maps， each capturing both fine and coarse structures. The fusion of the base layers is performed using a nuclear norm minimization （NNM） fusion rule， while the fusion of the feature maps employs the improved PCNN and regional energy-based rules. Experimental results demonstrate that the proposed method achieves average improvements of 47.6%， 5.2%， 6.4%， 9.4%， 5.3%， and 27.3% across spatial frequency， average gradient， correlation coefficient， information entropy， and standard deviation metrics， respectively. This method not only preserves the edge and texture information of the source images but also significantly enhances the visual quality of the fused images..