Research map / Image generation / Super-resolution
Joint EM Image Super-Resolution and Segmentation with Semantic and Structural Priors
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Proposes a joint framework for electron microscopy image super-resolution and neuron segmentation that couples the two tasks using semantic and structural priors.
Problem
EM image super-resolution accelerates connectome acquisition for downstream neuron segmentation, but existing methods do not couple SR with segmentation: SR lacks segmentation guidance and segmentation cannot adapt to super-resolved features.
Approach
Jointly trains EM super-resolution and segmentation with semantic and structural priors so each task guides the other, rather than running them as separate stages (abstract only; method details unavailable).
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- Learning Large-Factor EM Image Super-Resolution with Generative Priors (CVPR 2024 · similarity 0.88)
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