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Forget synthetic data: generative multimodal models can now hallucinate high-quality ground truth images from real-world degraded inputs, finally bridging the sim2real gap for image restoration.
Achieve high-quality semantic segmentation on low-quality images by injecting segmentation priors into image restoration, outperforming existing methods that focus solely on pixel-level fidelity.
