Comparison of Deep Learning Approaches for Extreme Low-SNR Image Restoration

Nasreen Elizabeth Buhn

doi:10.1093/gigascience/giag071

Comparison of Deep Learning Approaches for Extreme Low-SNR Image Restoration

2026-06-22

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AI

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Nasreen Elizabeth Buhn *

S

Sriya Reddy Adunur *

J

Joseph Hamilton *

S

Summer Levis *

G

Guy M Hagen *

J

Jonathan D Ventura *

DOI：10.1093/gigascience/giag071

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Live-cell fluorescence microscopy enables the study of dynamic cellular processes. However, fluorescence microscopy can damage cells and disrupt these dynamic processes through photobleaching and phototoxicity. Reducing a sample’s light exposure mitigates the effects of photobleaching and phototoxicity but results in low signal-to-noise ratio (SNR) images. Deep learning provides a solution for restoring these low-SNR images. However, these deep learning methods require large, representative datasets for training, testing, and benchmarking, as well as substantial GPU memory, particularly for denoising large images.

Comparison of Deep Learning Approaches for Extreme Low-SNR Image Restoration

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