We present an unsupervised learning denoising method, RepE (representation and
enhancement), designed for nonlinear optical microscopy images, such as second harmonic …

Deep neural networks can improve the quality of fluorescence microscopy images. Previous
methods, based on Convolutional Neural Networks (CNNs), require time-consuming training …

Fluorescence microscopy imaging speed is fundamentally limited by the measurement
signal-to-noise ratio (SNR). To improve image SNR for a given image acquisition rate …

Real-world microscopy data have a large amount of noise due to the limited light/electron
that can be used to capture images. The noise of microscopy data is composed of signal …

Stimulated Raman scattering (SRS) microscopy is a label-free quantitative chemical imaging
technique that has demonstrated great utility in biomedical imaging applications ranging …

Third harmonic generation (THG) microscopy shows great potential for instant pathology of
brain tumor tissue during surgery. However, due to the maximal permitted exposure of laser …

Fluorescence microscopy is a critically important imaging technique for biological studies,
enabling researchers to vi-sualize cellular structures and processes with high resolution and …

When using fluorescent microscopy to study cellular dynamics, trade-offs typically have to be
made between light exposure and quality of recorded image to balance the phototoxicity …

Low-dose imaging techniques have many important applications in diverse fields, from
biological engineering to materials science. Samples can be protected from phototoxicity or …

Fluorescence microscopy has enabled a dramatic development in modern biology. Due to
its inherently weak signal, fluorescence microscopy is not only much noisier than …