Recent years have witnessed great advances in applying deep learning to improve fluorescence microscopy imaging. However, enhancing the fidelity of image restoration networks and improving their ...

Volumetric fluorescence microscopy is an indispensable tool for comprehensive studies of cells and organs. Since the specimens are inherently three-dimensional (3D), the optimal imaging system should ...

In PhotoniX, researchers report a self-supervised deep learning method that denoises dynamic fluorescence images in vivo without requiring clean training data. The figure shows in vivo venule images ...

In life sciences, confocal fluorescence microscopy (CFM) is widely regarded for producing high-resolution cellular images. However, it requires fluorescent staining, which poses risks of ...

In recent years, fluorescence quenching microscopy (FQM) 1-3 has emerged as a viable technique that allows for the swift, cost-effective, and accurate imaging of two-dimensional (2D) materials like ...

Researchers commonly employ fluorescence microscopy to characterize molecular, cellular, and developmental processes in depth. Despite its benefits, scientists can face challenges when imaging their ...

FLIM principles. Schematic overview of fluorescence lifetime data acquisition and analysis in time−domain (td) and frequency−domain (fd) modes. In tdFLIM, photon arrival times are recorded after each ...

Stanford researchers have developed a microscope that can show how nanostructures interact inside living cells at the highest ...