Inference-Time Search using Side Information for Diffusion-based Image Reconstruction

• URL: http://arxiv.org/abs/2510.03352v1
• Date: Thu, 02 Oct 2025 20:16:00 GMT
• 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,
• Abstract summary: We propose a novel inference-time search algorithm that guides the sampling process using the side information.<n>Our approach can be seamlessly integrated into a wide range of existing diffusion-based image reconstruction pipelines.
• Score: 8.116163579233064
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion models have emerged 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 the side information in a manner that balances exploration and exploitation. This enables more accurate and reliable reconstructions, providing an alternative to the gradient-based guidance that is prone to reward-hacking artifacts. Our approach can be seamlessly integrated into a wide range of existing diffusion-based image reconstruction pipelines. Through extensive experiments on a number of inverse problems, such as box inpainting, super-resolution, and various deblurring tasks including motion, Gaussian, nonlinear, and blind deblurring, we show that our approach consistently improves the qualitative and quantitative performance of diffusion-based image reconstruction algorithms. We also show the superior performance of our approach with respect to other baselines, including reward gradient-based guidance algorithms. The code is available at \href{https://github.com/mhdfb/sideinfo-search-reconstruction}{this repository}.

Related papers

• Pseudo-View Enhancement via Confidence Fusion for Unposed Sparse-View Reconstruction [7.8268279161630785]
  3D scene reconstruction under unposed sparse viewpoints is a highly challenging yet practically important problem.<n>We propose a novel framework for sparse-view outdoor reconstruction that achieves high-quality results through bidirectional pseudo frame restoration.<n>Designs significantly enhance reconstruction completeness, suppress floating artifacts and improve overall geometric consistency under extreme view sparsity.
  arXiv  Detail & Related papers  (2026-02-25T03:45:47Z)
• From Image Denoisers to Regularizing Imaging Inverse Problems: An Overview [10.381324512554835]
  Inverse problems lie at the heart of modern imaging science, with broad applications in areas such as medical imaging, remote sensing, and microscopy.<n>Recent years have witnessed a paradigm shift in solving imaging inverse problems, where data-driven regularizers are used increasingly.<n>A notable approach for data-driven regularization is to use learned image denoisers as implicit priors in iterative image reconstruction algorithms.
  arXiv  Detail & Related papers  (2025-09-03T16:54:59Z)
• Diffusion Models for Solving Inverse Problems via Posterior Sampling with Piecewise Guidance [52.705112811734566]
  A novel diffusion-based framework is introduced for solving inverse problems using a piecewise guidance scheme.<n>The proposed method is problem-agnostic and readily adaptable to a variety of inverse problems.<n>The framework achieves a reduction in inference time of (25%) for inpainting with both random and center masks, and (23%) and (24%) for (4times) and (8times) super-resolution tasks.
  arXiv  Detail & Related papers  (2025-07-22T19:35:14Z)
• Learning Efficient and Effective Trajectories for Differential Equation-based Image Restoration [59.744840744491945]
  In this paper, we reformulate the trajectory optimization of this kind of method, focusing on enhancing both reconstruction quality and efficiency.<n>To mitigate the considerable computational burden associated with iterative sampling, we propose cost-aware trajectory distillation.<n>We fine-tune a foundational diffusion model (FLUX) with 12B parameters by using our algorithms, producing a unified framework for handling 7 kinds of image restoration tasks.
  arXiv  Detail & Related papers  (2024-10-07T07:46:08Z)
• Inverse Problems with Diffusion Models: A MAP Estimation Perspective [5.002087490888723]
  In Computer, several image restoration tasks such as inpainting, deblurring, and super-resolution can be formally modeled as inverse problems. We propose a MAP estimation framework to model the reverse conditional generation process of a continuous time diffusion model. We use our proposed framework to develop effective algorithms for image restoration.
  arXiv  Detail & Related papers  (2024-07-27T15:41:13Z)
• Deep Equilibrium Diffusion Restoration with Parallel Sampling [120.15039525209106]
  Diffusion model-based image restoration (IR) aims to use diffusion models to recover high-quality (HQ) images from degraded images, achieving promising performance. Most existing methods need long serial sampling chains to restore HQ images step-by-step, resulting in expensive sampling time and high computation costs. In this work, we aim to rethink the diffusion model-based IR models through a different perspective, i.e., a deep equilibrium (DEQ) fixed point system, called DeqIR.
  arXiv  Detail & Related papers  (2023-11-20T08:27:56Z)
• Diffusion Posterior Illumination for Ambiguity-aware Inverse Rendering [63.24476194987721]
  Inverse rendering, the process of inferring scene properties from images, is a challenging inverse problem. Most existing solutions incorporate priors into the inverse-rendering pipeline to encourage plausible solutions. We propose a novel scheme that integrates a denoising probabilistic diffusion model pre-trained on natural illumination maps into an optimization framework.
  arXiv  Detail & Related papers  (2023-09-30T12:39:28Z)
• A Variational Perspective on Solving Inverse Problems with Diffusion Models [101.831766524264]
  Inverse tasks can be formulated as inferring a posterior distribution over data. This is however challenging in diffusion models since the nonlinear and iterative nature of the diffusion process renders the posterior intractable. We propose a variational approach that by design seeks to approximate the true posterior distribution.
  arXiv  Detail & Related papers  (2023-05-07T23:00:47Z)
• Deep Variational Network Toward Blind Image Restoration [60.45350399661175]
  Blind image restoration is a common yet challenging problem in computer vision. We propose a novel blind image restoration method, aiming to integrate both the advantages of them. Experiments on two typical blind IR tasks, namely image denoising and super-resolution, demonstrate that the proposed method achieves superior performance over current state-of-the-arts.
  arXiv  Detail & Related papers  (2020-08-25T03:30:53Z)
• u-net CNN based fourier ptychography [5.46367622374939]
  We propose a new retrieval algorithm that is based on convolutional neural networks. Experiments demonstrate that our model achieves better reconstruction results and is more robust under system aberrations.
  arXiv  Detail & Related papers  (2020-03-16T22:48:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.