DS-DeBlur: Decoupled Supervision for Diffusion-based Image Deblurring

Real-world image deblurring remains challenging due to a fundamental mismatch between blur realism and ground-truth fidelity. Real datasets capture authentically blurred images but contain noisy references, whereas synthetic datasets offer clean supervision but fail to reflect realistic motion patterns. This discrepancy leads to a prior conflict with pretrained diffusion models, whose generative priors are learned from clean images, thereby limiting their effectiveness in real motion blur removal. To address this issue, we propose DS-DeBlur, a diffusion-based image deblurring model built upon a new Real-Synthetic Decoupled Supervision (ReSyDS) paradigm. The paradigm explicitly disentangles the learning of blur realism and supervision fidelity through two complementary paths. The synthetic path leverages clean paired data to activate diffusion priors and preserve structural fidelity, while the real path directly learns from authentically blurred images to promote perceptual realism and improve generalization without relying on imperfect references. To further support ReSyDS, we introduce an instance-driven, realistic blur synthesis method, a physically-grounded approach to generate high-quality and realistic blur–sharp pairs for effective supervision. Extensive experiments demonstrate that the proposed method performs favorably against state-of-the-art methods.