To improve the quality of the fusion of infrared and visible images， this study proposes a novel method based on structure and texture-aware Retinex （STAR）. It first decomposes the source images into reflection and illumination components according to the STAR model. This decomposition can separate the texture and structure of the source images accurately and extract the detailed features of the visible images with low luminance. Subsequently， it merges the reflection component using a weight map， which is constructed using the second-order gradient of the source images as the input. Moreover， it merges the illumination component using a gamma function， which can make the fused image have more brightness information. Finally， it reconstructs the fused reflection and illumination components to obtain the final fusion image. According to the test on 38 pairs of widely used images in the TNO infrared and visible image database， the proposed method can generate excellent fused results with high visual quality. Furthermore， compared with five state-of-the-art methods for the fusion of infrared and visible images， the proposed method achieved significantly better objective evaluation results in mutual information， nonlinear correlation information entropy， and feature measurement based on image phase consistency. This study involves the use of STAR model for fusing infrared and visible images and establishes a direct fusion framework based on Retinex， which improves the fusion results of the existing methods in terms of detailed features and global contrast.