arXiv:2510.03352v3 Announce Type: replace-cross Abstract: Diffusion models have been used as 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 framework that incorporates side information into existing diffusion-based inverse problem solvers via inference-time search, in a plug-and-play, training-free manner. Through extensive experiments across a range of inverse problems, including inpainting, super-resolution
The rapid advancement of diffusion models in AI is pushing researchers to explore more efficient and high-quality reconstruction methods for complex inverse problems.
This work introduces a training-free, plug-and-play method to enhance diffusion model performance using external information, which could significantly improve the practical application of AI in image reconstruction and related fields.
Diffusion models can now leverage 'side information' to produce superior results in challenging inverse problems without requiring retraining, potentially broadening their utility and efficiency.
- · AI researchers
- · Computer vision sector
- · Medical imaging sector
- · Manufacturing (quality control)
- · Existing less efficient reconstruction methods
Improved accuracy and efficiency in AI-driven image and data reconstruction tasks.
Accelerated development and deployment of AI solutions in fields requiring high-fidelity image reconstruction, such as scientific discovery and industrial inspection.
Potentially democratized access to advanced AI reconstruction capabilities due to the training-free nature of the method, reducing expertise and computational requirements.
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Read at arXiv cs.LG