OSDM-MReg: Multimodal Image Registration based One Step Diffusion Model

Multimodal remote sensing image registration aligns images from different sensors for data fusion and analysis. However, current methods often fail to extract modality-invariant features when aligning image pairs with large nonlinear radiometric differences. To address this issues, we propose OSDM-MReg, a novel multimodal image registration framework based image-to-image translation to eliminate the gap of multimodal images. Firstly, we propose a novel one-step unaligned target-guided conditional denoising diffusion probabilistic models(UTGOS-CDDPM)to translate multimodal images into a unified domain. In the inference stage, traditional conditional DDPM generate translated source image by a large number of iterations, which severely slows down the image registration task. To address this issues, we use the unaligned traget image as a condition to promote the generation of low-frequency features of the translated source image. Furthermore, during the training stage, we add the inverse process of directly predicting the translated image to ensure that the translated source image can be generated in one step during the testing stage. Additionally, to supervised the detail features of translated source image, we propose a new perceptual loss that focuses on the high-frequency feature differences between the translated and ground-truth images. Finally, a multimodal multiscale image registration network (MM-Reg) fuse the multimodal feature of the unimodal images and multimodal images by proposed multimodal feature fusion strategy. Experiments demonstrate superior accuracy and efficiency across various multimodal registration tasks, particularly for SAR-optical image pairs.

Multi-Domain Features Guided Supervised Contrastive Learning for Radar Target Detection

Detecting small targets in sea clutter is challenging due to dynamic maritime conditions. Existing solutions either model sea clutter for detection or extract target features based on clutter-target echo differences, including statistical and deep features. While more common, the latter often excels in controlled scenarios but struggles with robust detection and generalization in diverse environments, limiting practical use. In this letter, we propose a multi-domain features guided supervised contrastive learning (MDFG_SCL) method, which integrates statistical features derived from multi-domain differences with deep features obtained through supervised contrastive learning, thereby capturing both low-level domain-specific variations and high-level semantic information. This comprehensive feature integration enables the model to effectively distinguish between small targets and sea clutter, even under challenging conditions. Experiments conducted on real-world datasets demonstrate that the proposed shallow-to-deep detector not only achieves effective identification of small maritime targets but also maintains superior detection performance across varying sea conditions, outperforming the mainstream unsupervised contrastive learning and supervised contrastive learning methods.