Fine-tuning Stable Diffusion's U-Net with a confidence-weighted noise loss translates SAR to optical imagery with large FID and LPIPS gains over GAN and diffusion baselines on three datasets.
Explainable, physics-aware, trustworthy artificial intelligence: A paradigm shift for syn- thetic aperture radar
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C-DiffSET: Leveraging Latent Diffusion for SAR-to-EO Image Translation with Confidence-Guided Reliable Object Generation
Fine-tuning Stable Diffusion's U-Net with a confidence-weighted noise loss translates SAR to optical imagery with large FID and LPIPS gains over GAN and diffusion baselines on three datasets.