A Lightweight Deep Learning Framework for Fast, Real-Time Super-Resolution Fluctuation Imaging

Area of Science:

• Biophysics
• Cell Biology
• Microscopy

Background:

• Live-cell imaging demands high spatiotemporal resolution beyond the diffraction limit.
• Current super-resolution methods struggle with real-time acquisition due to high frame requirements and complex processing.
• Deep learning offers potential for faster imaging but often involves high latency and complex models.

Purpose of the Study:

• To develop a deep learning framework for real-time super-resolution fluctuation imaging.
• To improve temporal resolution and reduce computational demands in super-resolution microscopy.
• To enable gentle, low-light live-cell imaging for long-term studies.

Main Methods:

• Utilized a lightweight recurrent neural network (RNN) integrating sequential low-resolution frames.
• Developed RESURF (Real-time Super-resolution Fluctuation imaging framework) with simulation-based training.
• Employed transfer learning with small datasets for adaptability across microscope setups.

Main Results:

• Achieved significant improvement in temporal resolution, requiring as few as 8 frames.
• Doubled spatial resolution with inference times under 30 ms.
• Demonstrated suitability for live-cell imaging under low signal-to-noise ratio and low laser intensity conditions.
• Showcased generalization across diverse biological structures and microscope configurations.

Conclusions:

• RESURF provides a practical, low-latency deep learning solution for real-time super-resolution microscopy.
• The framework facilitates high-throughput and smart live-cell imaging.
• The developed dataset serves as a benchmark for fluctuation-based super-resolution techniques.