KeypointDiff: Keypoints-Guided Diffusion Model for Unpaired Object-Level SAR-to-Optical Aircraft Image Translation

Synthetic Aperture Radar (SAR) imagery provides all-weather, all-day, and high-resolution imaging capabilities but its unique imaging mechanism abstract imagery that severely lacks the high-fidelity contours and textures essential for automated interpretation and demanding pixel-level downstream tasks. Translating SAR images into optical images is a promising solution to enhance interpretation and support downstream tasks. Most existing research focuses on scene-level translation, with limited work on object-level translation due to the scarcity of paired data and the challenge of accurately preserving contour and texture details. To address these issues, this study proposes a keypoint-guided diffusion model KeypointDiff for SAR-to-optical image translation of unpaired aircraft targets. leverages keypoints as modality-agnostic structural anchors, enabling a novel training strategy that establishes structural-level correspondence between the unpaired SAR and optical domains. Based on the classifier-free guidance diffusion architecture, a class-angle guidance module (CAGM) is designed to integrate class and angle information into the diffusion generation process. Furthermore, a detector-based supervision loss and a visual consistency loss are employed to improve image fidelity and detail quality, tailored for aircraft targets. During sampling, aided by a pre-trained keypoint detector, the model eliminates the requirement for manually labeled class and azimuth information, enabling automated SAR-to-optical translation. Experimental results demonstrate that the proposed method outperforms existing approaches across multiple metrics, providing an efficient and effective solution for object-level SAR-to-optical translation and pixel-level detail recovery. Moreover, the method exhibits strong zero-shot generalization to untrained aircraft types, highlighting the model's practical applicability.