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Brain-inspired diffusion model reconstructs images from fMRI data

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Researchers have developed Hi-DREAM, a novel brain-inspired hierarchical diffusion framework designed to improve the reconstruction of natural images from fMRI data. This method leverages the hierarchical organization of the visual cortex by conditioning diffusion models on distinct visual Regions of Interest (ROI) streams. By converting these streams into a multi-scale cortical pyramid and using a ROI-conditioned ControlNet, Hi-DREAM injects anatomy-aware priors into the denoising process. Experiments on the Natural Scenes Dataset (NSD) demonstrate that Hi-DREAM achieves state-of-the-art semantic reconstruction while maintaining structural integrity, with analyses confirming the effectiveness of its hierarchy-aware conditioning and the complementary contributions of different ROI streams. AI

IMPACT This research advances the field of neuroimaging by improving the ability to reconstruct visual information from brain activity, potentially aiding in understanding visual processing.

RANK_REASON The cluster contains an academic paper detailing a new method for fMRI-to-image reconstruction. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

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Brain-inspired diffusion model reconstructs images from fMRI data

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The cluster contains an academic paper detailing a new method for fMRI-to-image reconstruction. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Guowei Zhang, Yun Zhao, Kai Sun, Moein Khajehnejad, Adeel Razi, Dinh Phung, Levin Kuhlmann ·

    Hi-DREAM: Brain-Inspired Hierarchical Diffusion for fMRI-to-Image Reconstruction via ROI Encoder and VisuAl Mapping

    arXiv:2511.11437v2 Announce Type: replace Abstract: Reconstructing natural images from fMRI requires bridging neural activity with both the structural and semantic representations used by modern generative models. Existing diffusion-based decoders often condition on a single glob…

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