Comparison of Deep Learning Approaches for Extreme Low-SNR Image Restoration
Our dataset provides a diverse benchmark for evaluating deep learning denoising methods, and our stitching method provides a solution to GPU memory constraints encountered when processing large images. Among the evaluated deep learning models, the supervised transformer-based model had the highest denoising performance but required the longest training time.
Authors
- Jonathan Ventura (ORCID: https://orcid.org/0000-0003-1661-8529)
- Joseph R. Hamilton
- Summer Levis
- Guy M Hagen
- Nasreen Elizabeth Buhn
- Sriya Reddy Adunur
Institutions
- California Polytechnic State University (US)
- University of Colorado Colorado Springs (US)
Publication Details
- Journal
- bioRxiv (Cold Spring Harbor Laboratory)
- Published
- 2026-01-20
- DOI
- https://doi.org/10.64898/2026.01.16.700026
- Primary Topic
- Cell Image Analysis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Institutes of Health