To address the detection challenges of infrared images, such as a low signal-to-noise ratio, blurred edge information, and clutter interference, a generative adversarial network infrared image denoising method based on subspace projection is proposed. First, the generator consists of a U-Net structure and a subspace attention network. The encoding stage extracts image features through four layers of downsampling, while the decoding stage reconstructs the image through four layers of upsampling. Second, a subspace projection network is added to each skip connection, and the feature maps of each layer are combined with upsampled images from the same layer to form a subspace projection network for image feature fusion. The projected feature maps are then fused with the original high-level features to achieve image denoising. Finally, the image is input to the discriminator for adversarial training to obtain a clear reconstructed image. The comparative experiments with BM3D（Block-Matching and 3D Filtering）, DnCNN（Deep Neural Neural Network For Image Denoising）, and other algorithms show that the improved generative adversarial network algorithm has better objective evaluation index effects, with PSNR and SSIM reaching 34.36 dB and 0.985 2, respectively, thus verifying the strong denoising performance of this algorithm.