Aiming at the problem of low resolution and unclear details of infrared polarization imaging of sea ships, a method combining wavelet transform and generative adversarial network is proposed to improve image resolution. Firstly, the pure convolutional neural network model (ConvNeXt) is used to improve the super-resolution network (SRGAN), and the original low-resolution ship infrared polarimetric image is denoised by using non-local mean. Then, the low-resolution image is initially super-resolved with the improved SRGAN, and the detail information of the initial super-resolved image is extracted using a two-dimensional discrete wavelet transform. Finally, the detail information is fused with the original low-resolution ship infrared polarization image through the inverse wavelet transform. Compared with the traditional super-resolution method, the peak signal-to-noise ratio and structural similarity of the super-resolution image obtained by the proposed method are significantly improved. In this paper, the infrared polarization image super-resolution and detail information fusion isachieved at the same time and the obtained super-resolution image not only retains the infrared polarization information of the original image, but also fuses the high-resolution detail information.