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Research questionHow can diffusion priors reconstruct images from linear measurements while balancing fidelity and diversity?Linear inverse problems provide incomplete or degraded measurements, so reconstructions must respect observed data while resolving ambiguity with a learned image prior. Efficiently sampling plausible solutions is difficult because strict data consistency can conflict with preserving realistic variation.
Computer Vision
Diffusion Models
Image & Video Processing
Inference Optimization
Machine Learning
Latest papersRecent research connected to this question, newest first.A Posterior-Dynamics Framework for Imaging Inverse Problems with Pretrained Diffusion PriorsThe source studies pretrained diffusion models as priors for linear imaging problems, including deblurring, super-resolution, and inpainting. It reports theoretical consistency and tracking results, along with experiments on reconstruction quality, coarse-grid stability, score-evaluation cost, and controllable fidelity–diversity behavior.research paper · Sep 3, 2026
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