ArticleScientific reports2024
Convolutional neural network transformer (CNNT) for fluorescence microscopy image denoising with improved generalization and fast adaptation.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Deep Learning Integration in Optical Microscopy: Advancements and Applications.Microscopy research and technique · 2026Review
- BDNet: A Real-Time Biomedical Image Denoising Network with Gradient Information Enhancement Loss.Biosensors · 2026Article
- Self-Supervised Contrastive Learning and GAN-Based Denoising for High-Fidelity HumanNeRF Images.Sensors (Basel, Switzerland) · 2025Article
- Deep-Learning-Assisted SICM for Enhanced Real-Time Imaging of Nanoscale Biological Dynamics.Small methods · 2025Article
- Article
- SNRAware: Improved Deep Learning MRI Denoising with SNR Unit Training and G-factor Map Augmentation.ArXiv · 2025Article
- Deep learning enhanced light sheet fluorescence microscopy for in vivo 4D imaging of zebrafish heart beating.Light, science & applications · 2025Article
- AI-based hardware and software tools in microscopy to boost research in immunology and virology.Frontiers in immunology · 2025Review
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12 authors.
Funding
Abstract
Deep neural networks can improve the quality of fluorescence microscopy images. Previous methods, based on Convolutional Neural Networks (CNNs), require time-consuming training of individual models for each experiment, impairing their applicability and generalization. In this study, we propose a novel imaging-transformer based model, Convolutional Neural Network Transformer (CNNT), that outperforms CNN based networks for image denoising. We train a general CNNT based backbone model from pairwise high-low Signal-to-Noise Ratio (SNR) image volumes, gathered from a single type of fluorescence microscope, an instant Structured Illumination Microscope. Fast adaptation to new microscopes is achieved by fine-tuning the backbone on only 5-10 image volume pairs per new experiment. Results show that the CNNT backbone and fine-tuning scheme significantly reduces training time and improves image quality, outperforming models trained using only CNNs such as 3D-RCAN and Noise2Fast. We show three examples of efficacy of this approach in wide-field, two-photon, and confocal fluorescence microscopy.
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