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[2510.03352] Inference-Time Search Using Side Information for Diffusion-Based Image Reconstruction
Summary
This article presents a novel inference-time search algorithm that enhances diffusion-based image reconstruction by utilizing side information, demonstrating significant improvements across various inverse problems.
Why It Matters
The research addresses a critical gap in existing diffusion models by incorporating side information, which can lead to better reconstruction quality in challenging scenarios. This advancement is particularly relevant for fields like medical imaging and computer vision, where high-quality reconstructions are essential.
Key Takeaways
- Introduces a new inference-time search algorithm for image reconstruction.
- Utilizes side information to significantly improve reconstruction quality.
- Demonstrates effectiveness across various inverse problems like inpainting and super-resolution.
- Framework can be integrated into existing diffusion pipelines without retraining.
- Outperforms traditional methods of incorporating side information.
Computer Science > Computer Vision and Pattern Recognition arXiv:2510.03352 (cs) [Submitted on 2 Oct 2025 (v1), last revised 17 Feb 2026 (this version, v2)] Title:Inference-Time Search Using Side Information for Diffusion-Based Image Reconstruction Authors:Mahdi Farahbakhsh, Vishnu Teja Kunde, Dileep Kalathil, Krishna Narayanan, Jean-Francois Chamberland View a PDF of the paper titled Inference-Time Search Using Side Information for Diffusion-Based Image Reconstruction, by Mahdi Farahbakhsh and 4 other authors View PDF HTML (experimental) 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...