RLI-DM: Robust Layout-Based Iterative Diffusion Model for SAR-to-RGB Image Translation

SAR-to-RGB translation, which transforms Synthetic Aperture Radar (SAR) images into visually interpretable RGB counterparts, is critical for enhancing applications in visual analysis, deep learning, and multi-source data fusion. However, existing methods often fail to preserve both global structural integrity and fine-grained local textures. This deficiency stems from weak feature extraction and the lack of a robust layout framework, leading to outputs with information loss, geometric distortions, and unnatural textures. To overcome these limitations, we propose the Robust Layout-based Iterative Diffusion Model (RLI-DM), a novel three-stage framework for high-fidelity translation. The framework begins with an Optical Reconstruction Module that employs a conditional diffusion model to ensure precise spectral mapping. At its core, the Geometric Robustness Module leverages a Brownian bridge model that we train to derive a noise-resilient layout, overcoming the limitations of conventional edge detection and significantly enhancing global structural fidelity. Finally, this robust layout guides a Customized Multi-Level Refinement Module to iteratively reconstruct local textures, ensuring structural clarity and cross-feature consistency. Extensive experiments on multiple benchmark datasets demonstrate that RLI-DM achieves state-of-the-art performance, significantly outperforming existing methods in both structural integrity and perceptual quality.