Both infrared and visible images have certain limitations, and relying on individual types of images cannot meet the practical needs of engineering. Instead, high-quality fused images can be obtained by introducing image fusion techniques. To better guarantee the diversity of the output information features, this study introduces a dual-branch convolutional neural network to achieve local fusion of infrared and visible images. Based on the dual-branch convolutional neural network, red and blue features are obtained from infrared images and visible light images simultaneously, thereby increasing the amount of information in the fusion image. The integer wavelet transform method is used for image compression. When the color-space model is built, the value of the t-factor is adjusted to obtain an ideal fusion image. The experimental results show that the edge information of the image after the fusion of this method is fully preserved, image detail information is enhanced, and fusion effect of infrared and visible images is improved, compared with the existing methods.