C-DiffSET: Leveraging Latent Diffusion for SAR-to-EO Image Translation with Confidence-Guided Reliable Object Generation

TL;DR

C-DiffSET is a novel SAR-to-EO image translation framework that leverages pretrained latent diffusion models and confidence-guided loss to produce more accurate and reliable optical images from SAR data, outperforming existing methods.

Contribution

The paper introduces C-DiffSET, which adapts pretrained latent diffusion models for SAR-to-EO translation and incorporates a confidence-guided loss for improved fidelity and structural accuracy.

Findings

Achieves state-of-the-art results on multiple datasets.

Significantly outperforms recent image-to-image translation methods.

Pretrained VAE aligns SAR and EO images in the same latent space.

Abstract

Synthetic Aperture Radar (SAR) imagery provides robust environmental and temporal coverage (e.g., during clouds, seasons, day-night cycles), yet its noise and unique structural patterns pose interpretation challenges, especially for non-experts. SAR-to-EO (Electro-Optical) image translation (SET) has emerged to make SAR images more perceptually interpretable. However, traditional approaches trained from scratch on limited SAR-EO datasets are prone to overfitting. To address these challenges, we introduce Confidence Diffusion for SAR-to-EO Translation, called C-DiffSET, a framework leveraging pretrained Latent Diffusion Model (LDM) extensively trained on natural images, thus enabling effective adaptation to the EO domain. Remarkably, we find that the pretrained VAE encoder aligns SAR and EO images in the same latent space, even with varying noise levels in SAR inputs. To further improve pixel-wise fidelity for SET, we propose a confidence-guided diffusion (C-Diff) loss that mitigates artifacts from temporal discrepancies, such as appearing or disappearing objects, thereby enhancing structural accuracy. C-DiffSET achieves state-of-the-art (SOTA) results on multiple datasets, significantly outperforming the very recent image-to-image translation methods and SET methods with large margins.