arXiv:2609.09832v1 Announce Type: new Abstract: Recent advances in high-resolution fluorescence and electron microscopy have enabled nanoscale imaging across increasingly large brain volumes, but the...
arXiv:2403. 18026v3 Announce Type: replace-cross Abstract: High-throughput imaging is often constrained by a trade-off between acquisition speed and image quality.
Catena is an open‑source, developer‑centric software suite designed to streamline large‑scale connectomics from electron microscopy data. It integrates modules for 3D neuron and organelle segmentation, synapse detection, microtubule tracking, and neurotransmitter inference into composable, chunk‑wise pipelines that are fully documented and extensible. The suite includes pretrained machine learning models, containerized runtimes, and shareable components to reduce compute and ground‑truth data needs while ensuring reproducible, scalable processing across workstations and clusters.
arXiv:2603. 13377v2 Announce Type: replace-cross Abstract: Representation learning has driven major advances in natural image analysis by enabling models to acquire high-level semantic features.
arXiv:2508. 05321v4 Announce Type: replace-cross Abstract: Assume you encounter an inverse problem that shall be solved for a large number of data, but no ground-truth data is available.
arXiv:2606. 14975v1 Announce Type: cross Abstract: How the wiring and functional organization of cortex shape recurrent computation remains a central question in both neuroscience and machine learning.
The paper introduces a spatial-frequency learning framework for enhancing Scanning Transmission Electron Microscopy (STEM) images. It employs a spatial-frequency fusion network that separately enhances features in the spatial and frequency domains before fusing them, leveraging periodic atomic arrangements in the frequency domain and local atomic details in the spatial domain. Additionally, the authors present a STEM noise calibration method to synthesize realistic images, creating a diverse dataset that improves performance on synthetic and real advanced material images, including better atomic localization accuracy.
arXiv:2609. 20562v1 Announce Type: new Abstract: Automated quality assessment, enhancement, and segmentation of multiple structures in $0.
The paper introduces a self‑supervised, physics‑aware deep learning method for hyperspectral image restoration and super‑resolution in biomedical imaging. It achieves 16× pixel super‑resolution and 12× faster imaging without external training data, preserving biological integrity across synthetic and experimental samples. The approach also reveals disease‑associated metabolic changes and offers physical insights into the model’s workings, with all code released as open source.
GigaBench is the first benchmark designed to evaluate learnable representations (LRs) for gigapixel image compression and super‑resolution. It compiles a diverse set of gigapixel images—collected, acquired, and synthetically rendered—from various domains and tests representative LR methods (Implicit, Hybrid, and Explicit) on consumer hardware. The study systematically measures reconstruction quality, model size, compression, and inference times, revealing consistent trends and offering actionable guidance for future research.
arXiv:2607. 10388v1 Announce Type: cross Abstract: Artificial intelligence (AI) is transforming electron microscopy by enabling quantitative analysis of increasingly large and complex datasets for nanoparticle characterization.
arXiv:2608.30768v1 Announce Type: new Abstract: Automatic neuron reconstruction from light microscopy images is a central problem in computational neuroanatomy. While recent methods have achieved enc...