AI super-resolution microscopy is rewriting the physical rules that have always constrained fluorescence imaging. Microscopists have long faced a fixed trade-off: push resolution or speed higher, and ...

By learning mappings from low-quality images to high-quality images, restoration networks reduce the photon dose required for microscopy imaging, thereby improving imaging speed and duration.

Deep neural networks such as UNet, RCAN and SwinIR have achieved remarkable success in image restoration and enhancement and have been widely adopted in fluorescence microscopy. By learning mappings ...