SSDDPM: A single SAR image generation method based on denoising diffusion probabilistic model

The limited availability of high-quality SAR images severely affects the accuracy and robustness of target detection, classification, and segmentation. To solve this problem, a novel image generation method based on a diffusion model is introduced that requires only one training sample to generate a realistic SAR image. We propose a single-scale architecture to avoid image noise accumulation. In addition, an attention module for the sampling layer in the generator for improving feature extraction is designed. Then, an information-guided attention module is proposed to suppress redundant information. Ship targets were selected as the research objects, and the proposed method was tested using an open-source dataset. We also built our own Sentinel-1 dataset to increase the number of challenges. The experimental results show that our method is optimal compared with the classical method SinGAN. Specifically, the SIFID is decreased from 4.80 × 10^(-4) to 1.66 × 10^(-7), the SSIM is improved from 0.07 to 0.51, and the LPIPS is decreased from 0.61 to 0.23. Compared with that of ExSinGAN, generation diversity increases by 27.35%.