אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Brownian Bridge Diffusion Models (BBDM) offer an appealing approach to image restoration by constructing a stochastic bridge between a clean image and its degraded observation. Although the bridge schedule plays an important role in reconstruction quality, it is typically chosen heuristically, motivating a principled framework for schedule design.

In this talk, an analytical framework for understanding how BBDM schedules affect the distribution of reconstructed images will be presented. Under a Mixture-of-Gaussians prior, we obtain a closed-form posterior and an ideal minimum mean squared error (MMSE) denoiser. We then compare the posterior law with a tractable surrogate for the BBDM reconstruction law. The surrogate preserves the posterior mean but exhibits a covariance deficit, revealing a coordinate-wise tradeoff between reconstruction fidelity, measured by mean squared error, and distributional accuracy, measured by Wasserstein distance.

Building on this analysis, we derive complementary schedule-design objectives and propose schedules based on analytical bounds that are independent of the prior and degradation. Extensive experiments on controlled MoG settings confirm full alignment between theory and practice, and experiments on the FFHQ dataset across inpainting, deblurring, and super-resolution tasks validate the practical value of our schedule-design criteria.