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| Home > Workflow collections > In process > muBRAND: Multi-Branch Diffusion for Flexible Generation and Inpainting of Image Series |
| Poster (After Call) | FZJ-2026-03284 |
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2026
Abstract: We introduce a unified diffusion model framework, muBRAND, for performing slice interpolation, cross-domain style transfer and damage repair in ordered image sequences such as serial sections, z-slices and temporal frames.The proposed architecture connects multiple generator branches to a shared contextual encoder. A multi-neighbor cross attention mechanism conditions each prediction on a predefined context of adjacent and distal slices of the same sequence.The framework provides a unified approach to address various restoration and translation tasks without per-task pretraining while handling arbitrary inter-slice spacings.We evaluate the framework on serial stacks of whole human brain sections imaged with a brightfield microscope; which are characterized by complex artifacts, missing sections, and interleaved stainings inducing strong style shifts between consecutive slices.Compared to task-specific baselines on image inpainting and style transfer, muBRAND improves reconstruction quality and plausibly imputes fully missing slices using context from both nearby and farther slices.To assess stack coherence beyond pixel fidelity, we introduce structure and biology oriented metrics that quantify continuity and morphological consistency of biological objects alongside standard generative metrics.
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