Inference-Time Search Using Side Information for Diffusion-Based Image Reconstruction

Code

• github.com/mahdi-farahbakhsh/diss
  OfficialIn paper★ 0

Abstract

Diffusion models have been widely used as powerful priors for solving inverse problems. However, existing approaches typically overlook side information that could significantly improve reconstruction quality, especially in severely ill-posed settings. In this work, we propose a novel inference-time search algorithm that guides the sampling process using side information. Our framework can be added to existing diffusion-based reconstruction pipelines in a plug-and-play manner, without requiring any training. Through extensive experiments across a range of inverse problems, including inpainting, super-resolution, and several deblurring tasks, and across multiple diffusion-based inverse problem solvers (DPS, DAPS, and MPGD), we show that augmenting each solver with our framework consistently improves the quality of the reconstructions over the corresponding original method. In order to demonstrate the generality of our approach, we consider diverse forms of side information, including reference images, textual descriptions, and anatomical MRI scans. We also show that our search-based approach outperforms other ways of incorporating side information, including reward gradient-based method. Code is available at here.

Reproductions