Pretrained models and training/testing data for the PSSR paper published in Nature Methods. Data are organized into two categories: main models for neural tissue, live cell, and neuronal mitochondria imaging, and supporting experiments comparing PSSR to other denoising methods. The data is hosted by the Texas Data Repository and was last updated on March 18, -2024.
Use Cases
- Training super-resolution models based on the provided EM and fluorescence microscopy data.
- Benchmarking PSSR performance against methods like BM3D, CARE, and Rolling Average as described.
- Applying pretrained PSSR models to enhance point-scanning microscopy images of neural tissue or mitochondria.
Strengths
- Data is directly associated with a peer-reviewed publication in Nature Methods.
- Includes both main models and supporting experiment data for comprehensive comparison.
- Covers multiple imaging modalities: electron microscopy (tSEM) and confocal fluorescence microscopy (ZEISS Airyscan).
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count and file sizes are unknown, which may limit suitability assessment.
- Description metadata is limited; actual data quality and structure require manual inspection after download.
Provenance
- Source
- Texas Data Repository Harvested Dataverse
- Collection Method
- Data collected for the research paper 'Deep learning-based point-scanning super-resolution imaging'.
- Time Range
- null
- Freshness
- Last updated 2024-03-18 11:41:25; freshness should be verified.
- Geography
- null