Abstract

Diffusion bridges (DB) have emerged as a promising alternative to diffusion models for imaging inverse problems, achieving faster sampling by directly bridging low- and high-quality image distributions. While incorporating measurement consistency has been shown to improve performance, existing DB methods fail to maintain this consistency in blind inverse problems, where the forward model is unknown. To address this limitation, we introduce ADOBI (Adaptive Diffusion Bridge for Inverse Problems), a novel framework that adaptively calibrates the unknown forward model to enforce measurement consistency throughout sampling iterations. Our adaptation strategy allows ADOBI to achieve high-quality parallel magnetic resonance imaging (PMRI) reconstruction in only 5–10 steps. Our numerical results show that ADOBI consistently delivers state-of-the-art performance, and further advances the Pareto frontier for the perception-distortion trade-off.

Adaptive Diffusion Bridge (ADOBI)

Figure 1: ADOBI is a novel diffusion bridge method for imaging inverse problems that adapts unknown forward models to enforce measurement consistency.

ADOBI for PMRI

Figure 2: Visual comparison of ADOBI with baseline methods on PMRI. The top row shows results for 4x accelerated PMRI data collection while the bottom row shows those 8x.

Perception-Distortion Tradeoff

Figure 3: Perception and distortion performance comparison. ADOBI consistently outperforms baselines across most NFE settings.

Uncertainty Quantification

Figure 4: Uncertainty quantification results. The variance is heavily correlated to reconstruction errors.

Bibtex

@ARTICLE{11457257,
  author={Hu, Yuyang and Peng, Albert and Gan, Weijie and Kamilov, Ulugbek S.},
  journal={IEEE Transactions on Computational Imaging}, 
  title={ADOBI: Adaptive Diffusion Bridge For Blind Inverse Problems with Application to MRI Reconstruction}, 
  year={2026},
  volume={},
  number={},
  pages={1-11},
  doi={10.1109/TCI.2026.3678407}}