Diffusion Posterior Sampling with Uncertain Forward Models

Key points

• Calibration and reference data can characterize uncertainty in the forward model's parameters, such as blur kernels or detector responses.1
• We introduce an uncertainty-aware likelihood (UAL), a Gaussian approximation to the marginal likelihood obtained by integrating over the uncertain parameters.1
• We compare samplers with and without UAL across six inverse problems spanning Gaussian and motion deblurring, CT, MRI, ultrasound, and fluorescence microscopy, and find improvements in reconstruction and ensemble prediction.1
• In motion deblurring, DAPS and PnP-DM with UAL also give 6.2 to 6.4 dB higher ensemble-mean PSNR than published BlindDPS and Fast Diffusion EM.1

Sources (1)

• [1]Diffusion Posterior Sampling with Uncertain Forward Models
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 02:47 AM

  Diffusion posterior samplers reconstruct signals by combining a pretrained diffusion prior with a measurement likelihood specified by a forward model and a noise model.

  Calibration and reference data can characterize uncertainty in the forward model's parameters, such as blur kernels or detector responses.

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